CODEWITHSUNDEEP
pythonpandas
Heathcliff
Heathcliff

Reputation: 3138

Pivot Pandas dataframe to see if condition is met

I have the following DataFrame that represents whether a User was present in some week, some year:

    User    Year    Week
0   John    2020    1
1   John    2020    2
2   Steve   2020    1
3   Fred    2020    3
4   George  2020    2   
5   George  2020    3
    ...     ...     ...
200 John    2021    2
201 John    2021    4
202 Steve   2021    2
203 Fred    2021    2
204 George  2021    1   
205 George  2021    4

I want to get a DataFrame that groups the dataset by User and each column represents whether he was present in a certain week of a certain year, each column either being of type boolean or integer with possible values 0 or 1.

It would look something like this:

        2020_1  2020_2  2020_3  ... 2021_1  2021_2  2021_3  2021_4
John         1       1       0  ...      0       1       0       1
Steve        1       0       0  ...      0       1       0       0
Fred         0       0       1  ...      0       1       0       0
George       0       1       1  ...      1       0       0       1

Is there anyway to do this without iterating through the DataFrme?

Thanks.

Upvotes: 2

Views: 68

Answers (3)

Arthur Borshenko
Arthur Borshenko

Reputation: 149

Here's one way you can do this:

import pandas as pd
df = pd.DataFrame({
    "User" : ["John","John","Steve","Fred","George","George"],
    "Year" : [2020,2020,2020,2020,2020,2020],
    "Week": [1,2,1,3,2,3]})

# add a helper column for year_week
df["year_week"] = df["Year"].map(str) + "_" + df["Week"].map(str)

# group by User and year_week, then unstack and fill NaN with 0
df.groupby(["User","year_week"]).size().unstack(fill_value = 0)

Results in:

| User   |   2020_1 |   2020_2 |   2020_3 |
|:-------|---------:|---------:|---------:|
| Fred   |        0 |        0 |        1 |
| George |        0 |        1 |        1 |
| John   |        1 |        1 |        0 |
| Steve  |        1 |        0 |        0 |

Upvotes: 3

Quang Hoang
Quang Hoang

Reputation: 150735

Create a new column and use pd.crosstab:

pd.crosstab(df['User'],
            df[['Year','Week']].astype(str).apply('_'.join, axis=1)
           )

Output:

col_0   2020_1  2020_2  2020_3  2021_1  2021_2  2021_4
User                                                  
Fred         0       0       1       0       1       0
George       0       1       1       1       0       1
John         1       1       0       0       1       1
Steve        1       0       0       0       1       0

Upvotes: 3

wwnde
wwnde

Reputation: 26676

pd.crosstab(df.User, df['Year'].astype(str)+"_"+df['Week'].astype(str))

Upvotes: 2

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