dfply | dplyr-style piping operations for pandas dataframes

by kieferk Python Version: Current License: GPL-3.0

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dplyr-style piping operations for pandas dataframes

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dfply Examples and Code Snippets

Groupby a column and then compare two other columns and return a value in a different column

Python

Lines of Code : 22

License : Strong Copyleft (CC BY-SA 4.0)

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mask = md.groupby('COMB', group_keys=False).apply(lambda x: x['FROM'].isin(x['TO']))
md = md.assign(Type=np.where(mask,"R","O"))
print (md)
    COMB FROM   TO Type
0   PNR1  MAA  BLR    R
1   PNR1  BLR  MAA    R
2  PNR11  DEL  MAA    O
3

How can I group by date in market_profile package in python?

Python

Lines of Code : 4

License : Strong Copyleft (CC BY-SA 4.0)

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df.groupby('day_month').apply(mp_va)

pd.concat([mp_va(group) for name, group in df.groupby('day_month')])

Error when creating a function using dfply @dfpipe

Python

Lines of Code : 35

License : Strong Copyleft (CC BY-SA 4.0)

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@dfpipe
def woe_iv(df, variable):
    return df >> group_by(X[variable]) >> summarize(COUNT=X[variable].count())

banks = pd.read_excel('banks.xlsx')

>> print(banks >> woe_iv('marital'))

   marital  COUNT
0  marri

Error when creating a function using dfply @dfpipe

Python

Lines of Code : 17

License : Strong Copyleft (CC BY-SA 4.0)

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import dfply
from dfply import *

@dfpipe
def woe_iv(df,variable):
    step1 = df>>group_by(X.variable)>>summarize(COUNT=X.variable.count())
    return step1

banks>>woe_iv(X.job)

@dfpipe
def woe_

How to avoid excessive lambda functions in pandas DataFrame assign and apply method chains

Python

Lines of Code : 22

License : Strong Copyleft (CC BY-SA 4.0)

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def df_apply(col_fn, *col_names):
    def inner_fn(df):
        cols = [df[col] for col in col_names]
        return col_fn(*cols)
    return inner_fn

new_table = (
    raw_data
        .assign(area=df_apply(calc_c

How can I use the mask command to include more than one parameter?

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

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data_test = data[data.year == 1997]

Converting R cumsum with reset to Python

Python

Lines of Code : 4

License : Strong Copyleft (CC BY-SA 4.0)

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df[''].cumsum() % 6

df.groupby('')[''].cumsum()

group and divide values in python

Python

Lines of Code : 11

License : Strong Copyleft (CC BY-SA 4.0)

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df['AVG_SALDO'] = df['SALDO'] / df.groupby('NROCUENTA').SALDO.transform('count')

Out[1112]:
     NROCUENTA   SALDO  AVG_SALDO
0    210-1-388  159.20      79.60
1    210-1-388  159.20      79.60
2   210-1-1219    0.93       0.93
3  210-1-1

Is there a way to exclude elements when importing a package?

Python

Lines of Code : 4

License : Strong Copyleft (CC BY-SA 4.0)

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__all__ = ["echo", "print" #Modules name go here

from module import *

Python equivalent to dplyr's ifelse

Python

Lines of Code : 10

License : Strong Copyleft (CC BY-SA 4.0)

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df['newCol'] = df.apply(lambda X: X.col1 - 2 if X.col2 == 'c' else X.col1 + 4, axis=1)
df

  col1 col2 newCol
0   1   a   5
1   2   b   6
2   3   c   1
3   4   d   8
4   5   e   9

Community Discussions

Trending Discussions on dfply

How can I group by date in market_profile package in python?

Error when creating a function using dfply @dfpipe

Python - Create a Bar Chart of Average Estimated Salary by c_rating, but do a facet_wrap by gender

Better use of apply to compute new values using actual iterated row and rows of same entire column

finding latest trip information from a large data frame

QUESTION

How can I group by date in market_profile package in python?

Asked 2021-Jun-21 at 10:40

I want to group by day so I can create a market profile for every day. This is my data frame. It's a usual OHLC data that goes from 2006-04-13 to 2021-06-14 and I add a day_month variable.

Python

...

ANSWER

Answered 2021-Jun-21 at 10:40

I think a good reference to understand this is the Group by: split-apply-combine user guide from the pandas doc.

There are several steps:

Splitting an object into groups

You already have this right: df.groupby('day_month') allows to split your global dataframe into smaller dataframes that have the same day_month value.
Applying a function to each group independently

Here there are a number of options. You could
- aggregate the data: reduce it to a sum of the values in the group for example,
- filter the data: select some of the groups,
- transform the data: which operates on columns in your group and returns columns of the same shape, for example normalize values within each group by subtracting the average and dividing by the standard deviation
- apply a function to the data: this is a catch-all (thus often slower than the other operations) that just passes each group of data to a function.
Applying a function is usually done by calling .agg(op), .filter(op), .transform(op), .apply(op) on the GroupBy result, where op that specifies which operation to perform. This can be a string that names a pre-defined operation(mean, sum, etc.) or a function (such as mp_va in your case). However you can not call directly GroupBy.mp_va(). This wouldn’t specify what kind of operation is being done.
Combining the data

This is just concatenating all the group-wise results into a single dataframe. This work is already done by agg, apply, and friends.

It seems here that your function applies to each group dataframe, and not to its columns individually. Therefore you need GroupBy.apply.

Basically you can think of the following code

Source https://stackoverflow.com/questions/68057925

QUESTION

Error when creating a function using dfply @dfpipe

Asked 2021-Apr-19 at 20:55

I have a dataset "banks" where if I do a groupby on a column name "jobs" to check counts in each category,I could find the following:

index jobs count 0 adnin. 478 1 blue-collar 946 2 entrepreneur 168 3 housemaid 112 4 management 969 5 retired 230 6 self-employed 183 7 services 417 8 student 84 9 technician. 768

I've also added first 3 lines of the dataset I am using: age,job,marital,education,default,balance,housing,loan,contact,day,month,duration,campaign,pdays,previous,poutcome,y 30,unemployed,married,primary,no,1787,no,no,cellular,19,oct,79,1,-1,0,unknown,no 33,services,married,secondary,no,4789,yes,yes,cellular,11,may,220,1,339,4,failure,no 35,management,single,tertiary,no,1350,yes,no,cellular,16,apr,185,1,330,1,failure,no

My intention is to create a small function which I can use for other columns as well hence I tried to create a function using "dfply" package.

...

ANSWER

Answered 2021-Apr-19 at 08:23

Shameek Mukherjee, is this a correct interpretation and indentation of your example code? I'm not able to find any difference besides the indentation.

Source https://stackoverflow.com/questions/67155771

QUESTION

Python - Create a Bar Chart of Average Estimated Salary by c_rating, but do a facet_wrap by gender

Asked 2020-Dec-15 at 23:04

My data set is Churn_Modeling:

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ANSWER

Answered 2020-Dec-15 at 23:04

Is this what you are trying to do?

Source https://stackoverflow.com/questions/65270025

QUESTION

Better use of apply to compute new values using actual iterated row and rows of same entire column

Asked 2020-Apr-21 at 01:51

Based on this example data :

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ANSWER

Answered 2020-Apr-21 at 01:51

Some points

The example you gave is a bit simple and I believe make it a bit harder to think in a more generic case. I then generated random data for 30 days using numpy.

By seeing the link you sent, I think they're showing us "how many days is the latest day that current day is double of apart from current_day".

To show this explicitly I will use very verbose column names in pandas and before calculating the metrics you want, I will build in the dataframe a reference list called days_current_day_is_double_of wich will for each row(day) calculate a list of days which the current deaths_cum is double of the day deaths_cum.

This column later can be substituted for a simple np.where() operation every time you want to find this for a row, if you don't want to keep a reference list in the dataframe. I think it's clearer keeping it.

generating data

Source https://stackoverflow.com/questions/61252437

QUESTION

finding latest trip information from a large data frame

Asked 2020-Mar-26 at 07:47

I have one requirement:

I have a dataframe "df_input" having 20M rows which includes trip details. columns are "vehicle-no", "geolocation","start","end". For each of the vehicle number there are multiple rows each having different geolocation for different trips.

Now I want to create a new dataframe df_final which will have only the first record for all of the vehicle-no. How can do that in efficient way?

I used something like below which is taking more than 5 hours to complete:

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ANSWER

Answered 2020-Mar-26 at 07:47

I think this will work out

Source https://stackoverflow.com/questions/60862658

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