map_one_column_to_another []

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==== map one series to another in a case insensitive manner ====

Given two dataframes
<code>
df1
        Item    Team
0    jetties  Team_1
1      Jetty  Team_2
2  Joint use  Team_1

df2
        Item
0      Jetty
1    Jetties
2  Joint use
</code>

map df2['Item'] to the corresponding Team using df1 as the lookup table.

Expected output (if case is included):
<code>
        Item Team_include_case
0      Jetty            Team_2
1    Jetties               NaN
2  Joint use            Team_1
</code>

Expected output (if case is ignored):
<code>
        Item Team_ignore_case
0      Jetty           Team_2
1    Jetties           Team_1
2  Joint use           Team_1
</code>

Solution:
<code>
 % cat 72994107_v4.py 
import pandas as pd

df1 = pd.DataFrame({
    "Item": ["jetties", "Jetty", "Joint use"],
    "Team": ["Team_1", "Team_2", "Team_1"]
})
print("df1")
print(df1)

df2 = pd.DataFrame({"Item": ["Jetty", "Jetties", "Joint use"]})
print("df2")
print(df2)


def map_series(s, df, key_col, value_col, ignore_case):
    if ignore_case:
        result = s.str.lower().map(
            df.assign(**{key_col: df[key_col].str.lower()})
              .set_index(key_col)[value_col]
        )
    else:
        result = s.map(df.set_index(key_col)[value_col])
    return result


df2["Team_include_case"] = map_series(
    df2["Item"], df1, "Item", "Team", ignore_case=False
)
df2["Team_ignore_case"] = map_series(
    df2["Item"], df1, "Item", "Team", ignore_case=True
)
print("after mapping")
print(df2)
</code>

Sample run:
<code>
 % python 72994107_v4.py
df1
        Item    Team
0    jetties  Team_1
1      Jetty  Team_2
2  Joint use  Team_1
df2
        Item
0      Jetty
1    Jetties
2  Joint use
after mapping
        Item Team_include_case Team_ignore_case
0      Jetty            Team_2           Team_2
1    Jetties               NaN           Team_1
2  Joint use            Team_1           Team_1
</code>

Ref:
  * https://stackoverflow.com/questions/72994107/pandas-using-map-and-ignore-case-sensitivity - I posted the above answer there.
  * https://stackoverflow.com/questions/39767718/pandas-assign-with-new-column-name-as-string - shows how to pass a string variable to the dataframe assign function

{{tag>["pandas dataframe assign string as variable"]}}
==== map one column to another ====
<code>
% ipython
Python 3.8.5 (default, Sep  4 2020, 07:30:14) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.18.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import pandas as pd
   ...: mapping = pd.DataFrame({'day': ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'], 'daynum': range(1, 8)})
   ...: print(mapping)
   day  daynum
0  Sun       1
1  Mon       2
2  Tue       3
3  Wed       4
4  Thu       5
5  Fri       6
6  Sat       7

In [2]: df = pd.DataFrame({'x': [5, 2, 7, 0, 1, 8], 'y': [59, 63, 66, 61, 46, 66]})
   ...: print(df)
   x   y
0  5  59
1  2  63
2  7  66
3  0  61
4  1  46
5  8  66

In [3]: df['z'] = df['x'].map(mapping.set_index('daynum')['day'])
   ...: print(df)
   x   y    z
0  5  59  Thu
1  2  63  Mon
2  7  66  Sat
3  0  61  NaN
4  1  46  Sun
5  8  66  NaN
</code>

See also: https://stackoverflow.com/questions/46049658/mapping-columns-from-one-dataframe-to-another-to-create-a-new-column - shows other possible solutions