pandas | powerful data analysis / manipulation library

# SciPy:
python -m pip install --no-cache --no-use-pep517 pythran cython pybind11 gast"==0.4.0"
pyenv rehash
python -m pip install --no-cache --no-binary :all: --no-use-pep517 scipy"==1.7.1"

# Scikit-Learn
python -m pip install --no-use-pep517 scikit-learn"==0.24.2"

brew install openblas openssl@1.1 pkg-config pyenv pyenv-virtualenv
python -m pip install numpy==1.19.5

-----------------------

# SciPy:
python -m pip install --no-cache --no-use-pep517 pythran cython pybind11 gast"==0.4.0"
pyenv rehash
python -m pip install --no-cache --no-binary :all: --no-use-pep517 scipy"==1.7.1"

# Scikit-Learn
python -m pip install --no-use-pep517 scikit-learn"==0.24.2"

brew install openblas openssl@1.1 pkg-config pyenv pyenv-virtualenv
python -m pip install numpy==1.19.5

-----------------------

    >> /opt/homebrew/bin/brew install openblas
    >> export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
    >> export CFLAGS="-falign-functions=8 ${CFLAGS}"
    >> git clone https://github.com/scipy/scipy.git
    >> cd scipy
    >> git submodule update --init
    >> /opt/homebrew/bin/pip3 install .
    >> /opt/homebrew/bin/pip3 install scikit-learn

-----------------------

RUN OPENBLAS="/opt/homebrew/opt/openblas" CFLAGS="-falign-functions=8 ${CFLAGS}" pip3 install scipy
RUN OPENBLAS="/opt/homebrew/opt/openblas" CFLAGS="-falign-functions=8 ${CFLAGS}" pip3 install scikit-learn

-----------------------

brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done

-----------------------

brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done

-----------------------

brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done

-----------------------

brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
# ^ no need to add to .zshrc, just doing this once.
pip install scikit-learn # ==0.24.1 if you want

Building wheels for collected packages: scikit-learn
  Building wheel for scikit-learn (pyproject.toml) ... done
  Created wheel for scikit-learn: filename=scikit_learn-1.0.1-cp38-cp38-macosx_12_0_arm64.whl size=6364030 sha256=0b0cc9a21af775e0c8077ee71698ff62da05ab62efc914c5c15cd4bf97867b31
Successfully built scikit-learn
Installing collected packages: scipy, scikit-learn
Successfully installed scikit-learn-1.0.1 scipy-1.7.3

Collecting click>=7.0
  Downloading click-8.0.3-py3-none-any.whl

Collecting grpcio>=1.28.1
  Downloading grpcio-1.42.0.tar.gz (21.3 MB)
     |████████████████████████████████| 21.3 MB 12.7 MB/s
  Preparing metadata (setup.py) ... done

## later in the process it installs using setuptools 
Running setup.py install for grpcio ... done

-----------------------

Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3

-----------------------

Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3

-----------------------

Python 3.9.10 (main, Jan 15 2022, 11:40:53) 
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
pip 22.0.3

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip install numpy==1.19.3
OPENBLAS="$(brew --prefix openblas)" pip install scipy==1.7.2

pip install cython
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export CXXFLAGS="$CXXFLAGS -I/opt/homebrew/Cellar/libomp/13.0.1/include"
export LDFLAGS="$LDFLAGS -L/opt/homebrew/Cellar/libomp/13.0.1/lib -lomp"
export DYLD_LIBRARY_PATH=/opt/homebrew/Cellar/libomp/13.0.1/lib
pip install scikit-learn==0.21.3

-----------------------

conda install --channel=conda-forge scikit-learn

def process_json(api_response): 
    
    def get_column_values(df):    
        return pd.concat([df, pd.json_normalize(df.pop('columns')).set_axis(df.index)], axis=1)
    
    def expand_children(df):
        if len(df.index) > 1:
            df['children'] = df['children'].fillna('').apply(lambda x: None if len(x) == 0 else x)
        df_children = df.pop('children').dropna().explode()
        if len(df_children.index) == 0: # return df if no children to append
            return df.index.names, df
        df_children = pd.json_normalize(df_children, max_level=0).set_axis(df_children.index).set_index('name', append=True)
        df_children = get_column_values(df_children)
        idx_names = list(df_children.index.names)
        idx_names[-1] = idx_names[-1] + '_' + str(len(idx_names))
        df[idx_names[-1]] = None
        return idx_names, pd.concat([df.set_index(idx_names[-1], append=True), df_children], axis=0)    
    
    columns_dict = pd.DataFrame(api_response['meta']['columns']).set_index('key').to_dict(orient='index') # save column definitions
    df = pd.DataFrame(api_response['data']['attributes']['total']['children']).set_index('name') # get initial dataframe     
    df = get_column_values(df) # get columns for initial level
    
    # expand children
    while 'children' in df.columns:
        idx_names, df = expand_children(df)
    
    # reorder/replace column headers and sort index
    df = (df.loc[:, [x for x in df.columns if x not in columns_dict.keys()] + list(columns_dict.keys())]
          .rename(columns={k:v['display_name'] for k,v in columns_dict.items()})
          .sort_index(na_position='first').reset_index())
    
    #collapse "name" columns (careful of potential duplicate rows)  
    for col in idx_names[::-1]:
            df[idx_names[-1]] = df[idx_names[-1]].fillna(df[col])
    df = df.rename(columns={'name': 'portfolio', idx_names[-1]: 'name'}).drop(columns=idx_names[1:-1])      
    
    return df

process_json(api_response)
54.2 ms ± 7.12 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

unpack_response(api_response) # iterrows
84.3 ms ± 9.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

-----------------------

def process_json(api_response): 
    
    def get_column_values(df):    
        return pd.concat([df, pd.json_normalize(df.pop('columns')).set_axis(df.index)], axis=1)
    
    def expand_children(df):
        if len(df.index) > 1:
            df['children'] = df['children'].fillna('').apply(lambda x: None if len(x) == 0 else x)
        df_children = df.pop('children').dropna().explode()
        if len(df_children.index) == 0: # return df if no children to append
            return df.index.names, df
        df_children = pd.json_normalize(df_children, max_level=0).set_axis(df_children.index).set_index('name', append=True)
        df_children = get_column_values(df_children)
        idx_names = list(df_children.index.names)
        idx_names[-1] = idx_names[-1] + '_' + str(len(idx_names))
        df[idx_names[-1]] = None
        return idx_names, pd.concat([df.set_index(idx_names[-1], append=True), df_children], axis=0)    
    
    columns_dict = pd.DataFrame(api_response['meta']['columns']).set_index('key').to_dict(orient='index') # save column definitions
    df = pd.DataFrame(api_response['data']['attributes']['total']['children']).set_index('name') # get initial dataframe     
    df = get_column_values(df) # get columns for initial level
    
    # expand children
    while 'children' in df.columns:
        idx_names, df = expand_children(df)
    
    # reorder/replace column headers and sort index
    df = (df.loc[:, [x for x in df.columns if x not in columns_dict.keys()] + list(columns_dict.keys())]
          .rename(columns={k:v['display_name'] for k,v in columns_dict.items()})
          .sort_index(na_position='first').reset_index())
    
    #collapse "name" columns (careful of potential duplicate rows)  
    for col in idx_names[::-1]:
            df[idx_names[-1]] = df[idx_names[-1]].fillna(df[col])
    df = df.rename(columns={'name': 'portfolio', idx_names[-1]: 'name'}).drop(columns=idx_names[1:-1])      
    
    return df

process_json(api_response)
54.2 ms ± 7.12 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

unpack_response(api_response) # iterrows
84.3 ms ± 9.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

pip install --upgrade jsonpath-ng

import json
import jsonpath_ng as jp
import pandas as pd

def unpack_response(r):
    # Create a dataframe from extracted data
    expr = jp.parse('$..children.[*]')
    data = [{'full_path': str(m.full_path), **m.value} for m in expr.find(r)]
    df = pd.json_normalize(data).sort_values('full_path', ignore_index=True)

    # Append a portfolio column
    df['portfolio'] = df.loc[df.full_path.str.contains(r'total\.children\.\[\d+]$'), 'name']
    df['portfolio'].fillna(method='ffill', inplace=True)

    # Deal with columns
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['full_path', 'portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols]

    return df

# Load the sample data from file
# with open('api_response_2022-02-13.json', 'r') as f:
#     api_response = json.load(f)

# Load the sample data from string
api_response = json.loads('{"meta": {"columns": [{"key": "value", "display_name": "Adjusted Value (No Div, USD)", "output_type": "Number", "currency": "USD"}, {"key": "time_weighted_return", "display_name": "Current Quarter TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "time_weighted_return_2", "display_name": "YTD TWR (USD)", "output_type": "Percent", "currency": "USD"}, {"key": "_custom_twr_audit_note_911328", "display_name": "TWR Audit Note", "output_type": "Word"}], "groupings": [{"key": "_custom_name_747205", "display_name": "* Reporting Client Name"}, {"key": "_custom_new_entity_group_453577", "display_name": "NEW Entity Group"}, {"key": "_custom_level_2_624287", "display_name": "* Level 2"}, {"key": "legal_entity", "display_name": "Legal Entity"}]}, "data": {"type": "portfolio_views", "attributes": {"total": {"name": "Total", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Falconer Family", "grouping": "_custom_name_747205", "columns": {"time_weighted_return": -0.046732301295604683, "time_weighted_return_2": -0.046732301295604683, "_custom_twr_audit_note_911328": null, "value": 23132492.905107163}, "children": [{"name": "Wealth Bucket A", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.045960317420568164, "time_weighted_return_2": -0.045960317420568164, "_custom_twr_audit_note_911328": null, "value": 13264448.506587159}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": [{"entity_id": 10604454, "name": "HUDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": 3.434094574039648e-06, "time_weighted_return_2": 3.434094574039648e-06, "_custom_twr_audit_note_911328": null, "value": 3337.99}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": [{"entity_id": 10604454, "name": "HUDG Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.025871339096964152, "time_weighted_return_2": -0.025871339096964152, "_custom_twr_audit_note_911328": null, "value": 1017004.7192636987}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": [{"entity_id": 10604454, "name": "HKDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370376329670656, "time_weighted_return_2": -0.030370376329670656, "_custom_twr_audit_note_911328": null, "value": 231142.67772000004}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": [{"entity_id": 10604454, "name": "HUDW Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05382756475465478, "time_weighted_return_2": -0.05382756475465478, "_custom_twr_audit_note_911328": null, "value": 9791282.570000006}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": [{"entity_id": 10604454, "name": "HJDJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01351630404081805, "time_weighted_return_2": -0.01351630404081805, "_custom_twr_audit_note_911328": null, "value": 2153366.6396034593}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": [{"entity_id": 10604454, "name": "HADJ Trust", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.002298190175237247, "time_weighted_return_2": -0.002298190175237247, "_custom_twr_audit_note_911328": null, "value": 68313.90999999999}, "children": []}]}]}, {"name": "Wealth Bucket B", "grouping": "_custom_new_entity_group_453577", "columns": {"time_weighted_return": -0.04769870075659244, "time_weighted_return_2": -0.04769870075659244, "_custom_twr_audit_note_911328": null, "value": 9868044.398519998}, "children": [{"name": "Asset Class A", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": 2.8632718065191298e-05, "time_weighted_return_2": 2.8632718065191298e-05, "_custom_twr_audit_note_911328": null, "value": 10234.94}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 2.82679297198829e-05, "time_weighted_return_2": 2.82679297198829e-05, "_custom_twr_audit_note_911328": null, "value": 244.28}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 4.9373572795108345e-05, "time_weighted_return_2": 4.9373572795108345e-05, "_custom_twr_audit_note_911328": null, "value": 5081.08}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.609603754315074e-06, "time_weighted_return_2": 6.609603754315074e-06, "_custom_twr_audit_note_911328": null, "value": 1523.62}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": 1.0999769004760296e-05, "time_weighted_return_2": 1.0999769004760296e-05, "_custom_twr_audit_note_911328": null, "value": 1828.9}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": 6.466673995619843e-06, "time_weighted_return_2": 6.466673995619843e-06, "_custom_twr_audit_note_911328": null, "value": 1557.06}, "children": []}]}, {"name": "Asset Class B", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.024645947842438676, "time_weighted_return_2": -0.024645947842438676, "_custom_twr_audit_note_911328": null, "value": 674052.31962}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.043304004172576405, "time_weighted_return_2": -0.043304004172576405, "_custom_twr_audit_note_911328": null, "value": 52800.96}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.022408434778798836, "time_weighted_return_2": -0.022408434778798836, "_custom_twr_audit_note_911328": null, "value": 599594.11962}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.039799855483646174, "time_weighted_return_2": -0.039799855483646174, "_custom_twr_audit_note_911328": null, "value": 7219.08}, "children": []}]}, {"name": "Asset Class C", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.03037038746301135, "time_weighted_return_2": -0.03037038746301135, "_custom_twr_audit_note_911328": null, "value": 114472.69744}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.030370390035505124, "time_weighted_return_2": -0.030370390035505124, "_custom_twr_audit_note_911328": null, "value": 114472.68744000001}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": 0, "time_weighted_return_2": 0, "_custom_twr_audit_note_911328": null, "value": 0.01}, "children": []}]}, {"name": "Asset Class D", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.06604362523792162, "time_weighted_return_2": -0.06604362523792162, "_custom_twr_audit_note_911328": null, "value": 5722529.229999997}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06154960593668424, "time_weighted_return_2": -0.06154960593668424, "_custom_twr_audit_note_911328": null, "value": 1191838.9399999995}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.06750460387418267, "time_weighted_return_2": -0.06750460387418267, "_custom_twr_audit_note_911328": null, "value": 4416618.520000002}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 38190.33}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.05604507809250081, "time_weighted_return_2": -0.05604507809250081, "_custom_twr_audit_note_911328": null, "value": 37940.72}, "children": []}]}, {"name": "Asset Class E", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.017118805423322003, "time_weighted_return_2": -0.017118805423322003, "_custom_twr_audit_note_911328": null, "value": 3148495.0914600003}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.015251157805867277, "time_weighted_return_2": -0.015251157805867277, "_custom_twr_audit_note_911328": null, "value": 800493.06146}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.01739609576880241, "time_weighted_return_2": -0.01739609576880241, "_custom_twr_audit_note_911328": null, "value": 2215511.2700000005}, "children": []}, {"entity_id": 10598341, "name": "Cht 11th Tr HBO Shirley", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02085132265594647, "time_weighted_return_2": -0.02085132265594647, "_custom_twr_audit_note_911328": null, "value": 44031.21}, "children": []}, {"entity_id": 10598337, "name": "Cht 11th Tr HBO Hannah", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.02089393244695803, "time_weighted_return_2": -0.02089393244695803, "_custom_twr_audit_note_911328": null, "value": 44394.159999999996}, "children": []}, {"entity_id": 10598334, "name": "Cht 11th Tr HBO Lau", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.020607507059866248, "time_weighted_return_2": -0.020607507059866248, "_custom_twr_audit_note_911328": null, "value": 44065.39000000001}, "children": []}]}, {"name": "Asset Class F", "grouping": "_custom_level_2_624287", "columns": {"time_weighted_return": -0.0014710489231547497, "time_weighted_return_2": -0.0014710489231547497, "_custom_twr_audit_note_911328": null, "value": 198260.12}, "children": [{"entity_id": 10868778, "name": "2012 Desc Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.0014477244560456848, "time_weighted_return_2": -0.0014477244560456848, "_custom_twr_audit_note_911328": null, "value": 44612.33}, "children": []}, {"entity_id": 10643052, "name": "2013 Irrev Tr HBO Thalia", "grouping": "legal_entity", "columns": {"time_weighted_return": -0.001477821083437858, "time_weighted_return_2": -0.001477821083437858, "_custom_twr_audit_note_911328": null, "value": 153647.78999999998}, "children": []}]}]}]}]}}, "included": []}}')

df = unpack_response(api_response)

print(df.iloc[:5:,1:])

print(df.iloc[:10,:3])

expr = jp.parse('$..children.[*]')

def unpack_response(r):
    df = pd.DataFrame()
    for _, r1 in pd.json_normalize(r, ['data', 'attributes', 'total', 'children']).iterrows(): 
        r1['portfolio'] = r1['name']
        df = df.append(r1)
        for _, r2 in pd.json_normalize(r1.children).iterrows(): 
            df = df.append(r2)
            for _, r3 in pd.json_normalize(r2.children).iterrows(): 
                df = df.append(r3).append(pd.json_normalize(r3.children))
    df['portfolio'].fillna(method='ffill', inplace=True)
    trans = {'columns.' + c['key']: c['display_name'] for c in r['meta']['columns']}
    cols = ['portfolio', 'name', 'entity_id', 'Adjusted Value (No Div, USD)', 'Current Quarter TWR (USD)', 'YTD TWR (USD)', 'TWR Audit Note']
    df = df.rename(columns=trans)[cols].reset_index(drop=True)
    return df

-----------------------

import pandas as pd
import json

# get the json file
json_dict = json.load(open('api_response_2022-02-13.json'))
    

# create a (nested) df out of it, and rename the 'top-level' name field to 'portfolio'
packed_df = pd.DataFrame.from_dict(json_dict['data']['attributes']['total']['children'])\
            .rename(columns={'name': 'portfolio'})

# expand the level-1 'children' (and call their 'name' field 'grand-parent')
unpacked_df = packed_df.groupby('portfolio')['children']\
              .apply(lambda x: pd.DataFrame(x.values[0])).reset_index()\
              .rename(columns={'name': 'grand_parent_name'})

# expand the level-2 'children' (and call their 'name' field 'parent')
unpacked_df = unpacked_df.groupby(['portfolio', 'grand_parent_name'])['children']\
              .apply(lambda x: pd.DataFrame(x.values[0])).reset_index()\
              .rename(columns={'name': 'parent_name'})

# expand the level-3 'children' (and keep their name as is)
unpacked_df = unpacked_df.groupby(['portfolio', 'grand_parent_name', 'parent_name'])['children']\
              .apply(lambda x: pd.DataFrame(x.values[0])).reset_index()

# expand the column field info from 'dict' to multiple columns
unpacked_df = pd.concat([unpacked_df.drop('columns', axis=1), pd.DataFrame(unpacked_df['columns'].tolist())], axis=1)

import numpy as np 
import pandas as pd
df =pd.DataFrame(np.random.rand(3,6))

with open("dump_from_v1.3.4.pickle", "wb") as f: 
    pickle.dump(df, f) 

quit()

import pickle

with open("dump_from_v1.3.4.pickle", "rb") as f: 
    df = pickle.load(f) 


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-ff5c218eca92> in <module>
      1 with open("dump_from_v1.3.4.pickle", "rb") as f:
----> 2     df = pickle.load(f)
      3 

AttributeError: Can't get attribute 'new_block' on <module 'pandas.core.internals.blocks' from '/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py'>

-----------------------

import numpy as np 
import pandas as pd
df =pd.DataFrame(np.random.rand(3,6))

with open("dump_from_v1.3.4.pickle", "wb") as f: 
    pickle.dump(df, f) 

quit()

import pickle

with open("dump_from_v1.3.4.pickle", "rb") as f: 
    df = pickle.load(f) 


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-ff5c218eca92> in <module>
      1 with open("dump_from_v1.3.4.pickle", "rb") as f:
----> 2     df = pickle.load(f)
      3 

AttributeError: Can't get attribute 'new_block' on <module 'pandas.core.internals.blocks' from '/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py'>

df = df.drop('market', axis=1)

df = df.drop(columns='market')

-----------------------

df = df.drop('market', axis=1)

df = df.drop(columns='market')

-----------------------

df.drop('market', axis=1)

df.drop(columns='market')

-----------------------

df.drop('market', axis=1)

df.drop(columns='market')

conda install python=3.9

-----------------------

conda update -n base -c defaults conda

import pandas as pd
import numpy as np

from thefuzz import process

def fuzzy_match(
    a: pd.DataFrame, b: pd.DataFrame, col: str, limit: int = 5, thresh: int = 80
):
    """use fuzzy matching to join on column"""

    s = b[col].tolist()

    matches = a[col].apply(lambda x: process.extract(x, s, limit=limit))
    matches = pd.DataFrame(np.concatenate(matches), columns=["match", "score"])

    # join other columns in b to matches
    to_join = (
        pd.merge(left=b, right=matches, how="right", left_on="City", right_on="match")
        .set_index(  # create an index that represents the matching row in df a, you can drop this when `limit=1`
            np.array(
                list(
                    np.repeat(i, limit if limit < len(b) else len(b))
                    for i in range(len(a))
                )
            ).flatten()
        )
        .drop(columns=["match"])
        .astype({"score": "int16"})
    )
    print(f"\t the index here represents the row in dataframe a on which to join")
    print(to_join)

    res = pd.merge(
        left=a, right=to_join, left_index=True, right_index=True, suffixes=("", "_b")
    )

    # return only the highest match or you can just set the limit to 1
    # and remove this
    df = res.reset_index()
    df = df.iloc[df.groupby(by="index")["score"].idxmax()].reset_index(drop=True)

    return df.drop(columns=["City_b", "score", "index"])

def test(df):

    expected = pd.DataFrame(
        {
            "City": ["San Francisco, CA", "Oakland, CA"],
            "Val": [1, 2],
            "Geo": ["geo1", "geo1"],
        }
    )

    print(f'{"expected":-^70}')
    print(expected)

    print(f'{"res":-^70}')
    print(df)

    assert expected.equals(df)


if __name__ == "__main__":

    a = pd.DataFrame({"City": ["San Francisco, CA", "Oakland, CA"], "Val": [1, 2]})
    b = pd.DataFrame(
        {"City": ["San Francisco-Oakland, CA", "Salinas, CA"], "Geo": ["geo1", "geo2"]}
    )

    print(f'\n\n{"fuzzy match":-^70}')
    res = fuzzy_match(a, b, col="City")
    test(res)

-----------------------

import numpy as np
import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity


# https://stackoverflow.com/a/27086669/8615419
# as a preprocessor for TfidfVectorizer
def clean_corpus(s: str):
    """return clean corpus -- replaced any non word chars with space"""
    for ch in ['\\','`','*','_','{','}','[',']','(',')','>','#','+','-','.','!','$','\'',',']:
        if ch in s:
            s = s.replace(ch, " ")
    return s.lower()

# why n-grams?
# this should account for any word misspellings
def fit_vectorizer(corpus: np.array, n: int = 3):

    vectorizer = TfidfVectorizer(analyzer="char_wb", preprocessor=clean_corpus, ngram_range=(n, n))
    tfidf = vectorizer.fit_transform(corpus)

    return tfidf, vectorizer


def cosine_similarity_join(a, b, col_name):

    a_len = len(a[col_name])

    # all of the "documents" in a 1D array
    corpus = np.concatenate([a[col_name].to_numpy(), b[col_name].to_numpy()])

    tfidf, vectorizer = fit_vectorizer(corpus, 3)
    # print(vectorizer.get_feature_names())

    # in this matrix each row represents the str in a and the col is the str from b, value is the cosine similarity
    res = cosine_similarity(tfidf[:a_len], tfidf[a_len:])
    print('in this matrix each row represents the str in a and the col is the str from b')
    print(res)

    res_series = pd.DataFrame(res).stack().rename("score")
    res_series.index.set_names(['a', 'b'], inplace=True)
    # print(res_series)
    
    # join scores to b
    b_scored = pd.merge(left=b, right=res_series, left_index=True, right_on='b').droplevel('b')
    # print(b_scored.sort_index())
   
    # find the indices on which to match, (highest score in each row)
    # best_match = np.argmax(res, axis=1)
    
    res = pd.merge(left=a, right=b_scored, left_index=True, right_index=True, suffixes=('', '_b'))
    print(res)

    df = res.reset_index()
    df = df.iloc[df.groupby(by="index")["score"].idxmax()].reset_index(drop=True)

    return df.drop(columns=["City_b", "score", "index"])  

def test(df):

    expected = pd.DataFrame(
        {
            "City": ["San Francisco, CA", "Oakland, CA"],
            "Val": [1, 2],
            "Geo": ["geo1", "geo1"],
        }
    )

    print(f'{"expected":-^70}')
    print(expected)

    print(f'{"res":-^70}')
    print(df)

    assert expected.equals(df)


if __name__ == "__main__":

    a = pd.DataFrame({"City": ["San Francisco, CA", "Oakland, CA"], "Val": [1, 2]})
    b = pd.DataFrame(
        {"City": ["San Francisco-Oakland, CA", "Salinas, CA"], "Geo": ["geo1", "geo2"]}
    )

    print(f'\n\n{"n-gram cosine similarity":-^70}')
    res = cosine_similarity_join(a, b, col_name="City")
    test(res)

-----------------------

# Python env: pip install thefuzz
# Anaconda env: pip install thefuzz
# -> thefuzz is not yet available on Anaconda (2021-09-18)
# -> you can use the old package: conda install -c conda-forge fuzzywuzzy

from thefuzz import process

best_city = lambda x: process.extractOne(x, df2["City"])[2]  # See note below
df1['Geo'] = df2.loc[df1["City"].map(best_city).values, 'Geo'].values

>>> df1
                City  Val   Geo
0  San Francisco, CA    1  geo1
1        Oakland, CA    2  geo1

-----------------------

# Python env: pip install thefuzz
# Anaconda env: pip install thefuzz
# -> thefuzz is not yet available on Anaconda (2021-09-18)
# -> you can use the old package: conda install -c conda-forge fuzzywuzzy

from thefuzz import process

best_city = lambda x: process.extractOne(x, df2["City"])[2]  # See note below
df1['Geo'] = df2.loc[df1["City"].map(best_city).values, 'Geo'].values

>>> df1
                City  Val   Geo
0  San Francisco, CA    1  geo1
1        Oakland, CA    2  geo1

# Results
res = []

# Nested loop: first for length of index col, then next for cols
for i in range(len(index_col)):
    for j in range(len(cols)):
        # Format of data
        res.append([index_col[i], cols[j], values[i][j]])

# Return results
print(res)
return res

-----------------------

data = []

for col, row in df.items():
    for ind, val in row.reset_index().values:
        data.append([ind, col, val])

data

-----------------------

df.unstack().swaplevel().reset_index().values.tolist()
#OR
df.reset_index().melt(id_vars="index").values.tolist()
# [['store1', 'January', 2],
#  ['store2', 'January', 5],
#  ['store3', 'January', 8],
#  ['store1', 'February', 3],
#  ['store2', 'February', 6],
#  ['store3', 'February', 9],
#  ['store1', 'March', 4],
#  ['store2', 'March', 7],
#  ['store3', 'March', 10]]

df.transpose().unstack().reset_index().values.tolist()
# [['store1', 'January', 2],
#  ['store1', 'February', 3],
#  ['store1', 'March', 4],
#  ['store2', 'January', 5],
#  ['store2', 'February', 6],
#  ['store2', 'March', 7],
#  ['store3', 'January', 8],
#  ['store3', 'February', 9],
#  ['store3', 'March', 10]]

-----------------------

df.unstack().swaplevel().reset_index().values.tolist()
#OR
df.reset_index().melt(id_vars="index").values.tolist()
# [['store1', 'January', 2],
#  ['store2', 'January', 5],
#  ['store3', 'January', 8],
#  ['store1', 'February', 3],
#  ['store2', 'February', 6],
#  ['store3', 'February', 9],
#  ['store1', 'March', 4],
#  ['store2', 'March', 7],
#  ['store3', 'March', 10]]

df.transpose().unstack().reset_index().values.tolist()
# [['store1', 'January', 2],
#  ['store1', 'February', 3],
#  ['store1', 'March', 4],
#  ['store2', 'January', 5],
#  ['store2', 'February', 6],
#  ['store2', 'March', 7],
#  ['store3', 'January', 8],
#  ['store3', 'February', 9],
#  ['store3', 'March', 10]]

-----------------------

lst = [[*k, v] for k, v in df.unstack().swaplevel().to_dict().items()]

-----------------------

temp = df.stack()

[[*ent, val] for ent, val in zip(temp.index, temp)]

[['store1', 'January', 2],
 ['store1', 'February', 3],
 ['store1', 'March', 4],
 ['store2', 'January', 5],
 ['store2', 'February', 6],
 ['store2', 'March', 7],
 ['store3', 'January', 8],
 ['store3', 'February', 9],
 ['store3', 'March', 10]]

-----------------------

>>> df.stack().reset_index().to_numpy()
array([['store1', 'January', 2],
       ['store1', 'February', 3],
       ['store1', 'March', 4],
       ['store2', 'January', 5],
       ['store2', 'February', 6],
       ['store2', 'March', 7],
       ['store3', 'January', 8],
       ['store3', 'February', 9],
       ['store3', 'March', 10]], dtype=object)
>>> 

>>> df.melt(ignore_index=False).reset_index().to_numpy()
array([['store1', 'January', 2],
       ['store2', 'January', 5],
       ['store3', 'January', 8],
       ['store1', 'February', 3],
       ['store2', 'February', 6],
       ['store3', 'February', 9],
       ['store1', 'March', 4],
       ['store2', 'March', 7],
       ['store3', 'March', 10]], dtype=object)
>>> 

-----------------------

>>> df.stack().reset_index().to_numpy()
array([['store1', 'January', 2],
       ['store1', 'February', 3],
       ['store1', 'March', 4],
       ['store2', 'January', 5],
       ['store2', 'February', 6],
       ['store2', 'March', 7],
       ['store3', 'January', 8],
       ['store3', 'February', 9],
       ['store3', 'March', 10]], dtype=object)
>>> 

>>> df.melt(ignore_index=False).reset_index().to_numpy()
array([['store1', 'January', 2],
       ['store2', 'January', 5],
       ['store3', 'January', 8],
       ['store1', 'February', 3],
       ['store2', 'February', 6],
       ['store3', 'February', 9],
       ['store1', 'March', 4],
       ['store2', 'March', 7],
       ['store3', 'March', 10]], dtype=object)
>>> 

  2021-10-31 10:47:22  (rev 3)
     bokeh  {2.3.3 (defaults/win-64) -> 2.4.1 (defaults/win-64)}
     click  {8.0.1 (defaults/noarch) -> 8.0.3 (defaults/noarch)}
     filelock  {3.0.12 (defaults/noarch) -> 3.3.1 (defaults/noarch)}
     freetype  {2.10.4 (defaults/win-64) -> 2.11.0 (defaults/win-64)}
     imagecodecs  {2021.6.8 (defaults/win-64) -> 2021.8.26 (defaults/win-64)}
     joblib  {1.0.1 (defaults/noarch) -> 1.1.0 (defaults/noarch)}
     lerc  {2.2.1 (defaults/win-64) -> 3.0 (defaults/win-64)}
     more-itertools  {8.8.0 (defaults/noarch) -> 8.10.0 (defaults/noarch)}
     pyopenssl  {20.0.1 (defaults/noarch) -> 21.0.0 (defaults/noarch)}
     scikit-learn  {0.24.2 (defaults/win-64) -> 1.0.1 (defaults/win-64)}
     statsmodels  {0.12.2 (defaults/win-64) -> 0.13.0 (defaults/win-64)}
     sympy  {1.8 (defaults/win-64) -> 1.9 (defaults/win-64)}
     tqdm  {4.62.2 (defaults/noarch) -> 4.62.3 (defaults/noarch)}
     xlwings  {0.24.7 (defaults/win-64) -> 0.24.9 (defaults/win-64)}

-----------------------

conda update -c anaconda numpy

conda upgrade -c conda-forge matplotlib

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

-----------------------

print(df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades'))

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

    Name  Age  Subject Grade
0    Bob   13  English     C
1   John   16  English     B
2    Foo   14  English     B
3    Bar   15  English    A+
4   Alex   17  English     F
5    Tom   12  English     A
6    Bob   13     Math    A+
7   John   16     Math     B
8    Foo   14     Math     A
9    Bar   15     Math     F
10  Alex   17     Math     D
11   Tom   12     Math     C

print(
    df.melt(
        id_vars=["Name", "Age"],
        value_vars="Math",
        var_name="Subject",
        value_name="Grades",
    )
)

print(
    df.set_index(["Name", "Age"])[["Math"]]
    .stack()
    .reset_index(name="Grade")
    .rename(columns={"level_2": "Subject"})
    .sort_values("Subject")
    .reset_index(drop=True)
)

   Name  Age Subject Grade
0   Bob   13    Math    A+
1  John   16    Math     B
2   Foo   16    Math     A
3   Bar   15    Math     F
4  Alex   15    Math     D
5   Tom   13    Math     C

print(
    df.melt(id_vars=["Name", "Age"])
    .groupby("value", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .rename(columns={"level_2": "Subjects", 0: "Grade"})
    .groupby("Grade", as_index=False)
    .agg(", ".join)
)

  Grade             Name                Subjects
0     A         Foo, Tom           Math, English
1    A+         Bob, Bar           Math, English
2     B  John, John, Foo  Math, English, English
3     C         Bob, Tom           English, Math
4     D             Alex                    Math
5     F        Bar, Alex           Math, English

df = df.melt(id_vars=['Name', 'Age'], var_name='Subject', value_name='Grades')

print(
    df.pivot_table("Grades", ["Name", "Age"], "Subject", aggfunc="first")
    .reset_index()
    .rename_axis(columns=None)
)

   Name  Age English Math
0  Alex   15       F    D
1   Bar   15      A+    F
2   Bob   13       C   A+
3   Foo   16       B    A
4  John   16       B    B
5   Tom   13       A    C

print(
    df.melt(id_vars=["Name", "Age"], var_name="Subject", value_name="Grades")
    .groupby("Name", as_index=False)
    .agg(", ".join)
)

print(
    df.set_index(["Name", "Age"])
    .stack()
    .reset_index()
    .groupby("Name", as_index=False)
    .agg(", ".join)
    .rename({"level_2": "Subjects", 0: "Grades"}, axis=1)
)

   Name       Subjects Grades
0  Alex  Math, English   D, F
1   Bar  Math, English  F, A+
2   Bob  Math, English  A+, C
3   Foo  Math, English   A, B
4  John  Math, English   B, B
5   Tom  Math, English   C, A

print(df.melt(var_name='Column', value_name='Value'))

print(
    df.stack()
    .reset_index(level=1)
    .sort_values("level_1")
    .reset_index(drop=True)
    .set_axis(["Column", "Value"], axis=1)
)

     Column Value
0       Age    16
1       Age    15
2       Age    15
3       Age    16
4       Age    13
5       Age    13
6   English    A+
7   English     B
8   English     B
9   English     A
10  English     F
11  English     C
12     Math     C
13     Math    A+
14     Math     D
15     Math     B
16     Math     F
17     Math     A
18     Name  Alex
19     Name   Bar
20     Name   Tom
21     Name   Foo
22     Name  John
23     Name   Bob

df.columns = df.columns.str.split('_', expand=True)
df.stack().rename_axis(['code', 'type'])

           click  view
code type             
1    A         3  3000
     B         5  1200
2    A        23  2300
     B         3   300

-----------------------

df.columns = df.columns.str.split('_', expand=True)
df.stack().rename_axis(['code', 'type'])

           click  view
code type             
1    A         3  3000
     B         5  1200
2    A        23  2300
     B         3   300

-----------------------

out = pd.wide_to_long(df.reset_index(),
                      ['view','click'],
                      i='Code',
                      j='type',
                      sep='_',
                      suffix='\\w+')
           view  click
Code type             
1    A     3000      3
2    A     2300     23
1    B     1200      5
2    B      300      3

-----------------------

df = pd.DataFrame({"Code":[1,2], "view_A":[3000, 2300], "click_A":[3, 23], 
                   "view_B":[1200, 300], "click_B":[5, 3]})

# pip install pyjanitor
import pandas as pd
import janitor

(df.pivot_longer(index='Code', 
                 names_to=('.value', 'type'), 
                 names_sep='_')
   .set_index(['Code', 'type'])
)

           view  click
Code type
1    A     3000      3
2    A     2300     23
1    B     1200      5
2    B      300      3

-----------------------

df = pd.DataFrame({"Code":[1,2], "view_A":[3000, 2300], "click_A":[3, 23], 
                   "view_B":[1200, 300], "click_B":[5, 3]})

# pip install pyjanitor
import pandas as pd
import janitor

(df.pivot_longer(index='Code', 
                 names_to=('.value', 'type'), 
                 names_sep='_')
   .set_index(['Code', 'type'])
)

           view  click
Code type
1    A     3000      3
2    A     2300     23
1    B     1200      5
2    B      300      3