pandasql | pandasql allows you to query pandas DataFrames using SQL | SQL Database library

df['street_name'] = df['street_name'].apply(lambda x: ', '.join(set(x.split(', '))

from collections import Counter
df['street_name'] = df['street_name'].apply(lambda x: ', '.join(Counter(x.split(', ')).keys()))

df['Date'] = pd.to_datetime(df['Date'])

g = df.groupby(["Card Number", pd.Grouper(key='Date', freq='30min')], sort=False)
df_out = g['Amount'].agg(['count', 'mean']).add_prefix('transactions30min_').reset_index()

import pandas as pd
from pandasql import sqldf
class MyAmaizingClass:
    def static_function():
        df1 = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['Jhon1', 'Jhon2', 'Jhon3']})
        df2 = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['

repository = self.gh.repository("kennethreitz", "requests")
forked_repo = repository.create_fork()
assert isinstance(forked_repo, github3.repos.Repository)

org_forked_repo = repository.create_fork("github3py")
assert isinstance(org_forked

sqls = {i: f"SELECT * FROM df WHERE Animal LIKE '{i}'" for i in searchStrings}

out = pd.concat({key: sqldf(qs) for key, qs in sqls.items()}, names=['sql', None]) \
        .droplevel(1).reset_index().drop_duplicates()

<

def func():
    for x, y in df2.groupby("id"):
        tmp = df1.loc[df1["id"].eq(x)]
        tmp.index = pd.IntervalIndex.from_arrays(tmp['start'], tmp['end'], closed='both')
        y[["start", "end"]] = tmp.loc[y.timestamp, ["start", "e