Exploratory Data Analysis with Pandas

Question

How can i group continuous data (like a tenure column showing different months for each row) into categories shown in a separate column using pandas

Answer 1

Are you looking for something like this?:

import datetime

import pandas as pd

# Make fake data
dates = {"tenure": [datetime.date(2020, 1, 31), datetime.date(2020, 1, 24), datetime.date(2020, 5, 13),
                    datetime.date(2021, 5, 23), datetime.date(2022, 5, 5), datetime.date(2020, 3, 16),
                    datetime.date(2020, 5, 28), datetime.date(2020, 9, 23), datetime.date(2020, 12, 28),
                    datetime.date(2021, 10, 12)]}
df = pd.DataFrame(data=dates)

tenure
2020-01-31
2020-01-24
2020-05-13
2021-05-23
2022-05-05
2020-03-16
2020-05-28
2020-09-23
2020-12-28
2021-10-12

# Make months to group by
df["tenure"] = pd.to_datetime(df.tenure)
df["month"] = df.tenure.dt.month_name()

tenure	month
2020-01-31 00:00:00	January
2020-01-24 00:00:00	January
2020-05-13 00:00:00	May
2021-05-23 00:00:00	May
2022-05-05 00:00:00	May
2020-03-16 00:00:00	March
2020-05-28 00:00:00	May
2020-09-23 00:00:00	September
2020-12-28 00:00:00	December
2021-10-12 00:00:00	October

# Group by months and show "different months for each row"
df = (df
      .sort_values("tenure")
      .groupby("month")["tenure"]
      .apply(lambda x: x.reset_index(drop=True))
      .unstack()
      .reset_index())

month	0	1	2	3
December	2020-12-28 00:00:00	NaT	NaT	NaT
January	2020-01-24 00:00:00	2020-01-31 00:00:00	NaT	NaT
March	2020-03-16 00:00:00	NaT	NaT	NaT
May	2020-05-13 00:00:00	2020-05-28 00:00:00	2021-05-23 00:00:00	2022-05-05 00:00:00
October	2021-10-12 00:00:00	NaT	NaT	NaT
September	2020-09-23 00:00:00	NaT	NaT	NaT

OR perhaps something like this?:

# Group by months and show "different months for each row"
df = (df.sort_values("tenure")
        .groupby("month")["tenure"]
        .apply(lambda x: x.reset_index(drop=True))
        .unstack()
        .reset_index()
        .T)
df = df.rename(columns=df.iloc[0]).drop(df.index[0]).reset_index(drop=True)

December	January	March	May	October	September
2020-12-28 00:00:00	2020-01-24 00:00:00	2020-03-16 00:00:00	2020-05-13 00:00:00	2021-10-12 00:00:00	2020-09-23 00:00:00
NaT	2020-01-31 00:00:00	NaT	2020-05-28 00:00:00	NaT	NaT
NaT	NaT	NaT	2021-05-23 00:00:00	NaT	NaT
NaT	NaT	NaT	2022-05-05 00:00:00	NaT	NaT