如何根据 ID 和指向 df2 中列名称的值从另一个 dataframe (df2) 中的列中获取值。 蟒蛇/熊猫

Question

So I have this df1:

ID    State
1      AA
2      AA
3      ZF
3      CJ

and df2:

ID    AA    ZF  CJ  etc
1     9      8  77
2     7      6   5
3     8     88   6

I have to create a new column in df1 bringing the values in df2 like this:

ID    State    Value
1      AA       9
2      AA       7
3      ZF       88
3      CJ       6

I've been trying for 2 hours now and I can't seem to find a way to refer to the column names on df2 based on the values of df1['State']. Even if I could think of a way to do that, the value is filtered by ID too... tricky stuff. Any help?

Thank you in advance

Answer 1

You can melt() on ID and merge() with df1 :

df1 = df1.merge(df2.melt('ID', var_name='State', value_name='Value'))

#    ID State  Value
# 0   1    AA      9
# 1   2    AA      7
# 2   3    ZF     88
# 3   3    CJ      6

---

This is slower and more brittle, but if you set df2 's index to ID , you can use loc[] in an apply() :

df2 = df2.set_index('ID')
df1['Value'] = df1.apply(lambda x: df2.loc[x.ID, x.State], axis=1)

Answer 2

Let's try something like:

import pandas as pd

df1 = pd.DataFrame({'ID': {0: 1, 1: 2, 2: 3, 3: 3},
                    'State': {0: 'AA', 1: 'AA',
                              2: 'ZF', 3: 'CJ'}})
df2 = pd.DataFrame({'ID': {0: 1, 1: 2, 2: 3},
                    'AA': {0: 9, 1: 7, 2: 8},
                    'ZF': {0: 8, 1: 6, 2: 88},
                    'CJ': {0: 77, 1: 5, 2: 6}})

merged = df1.merge(
    df2.set_index('ID')
        .stack()
        .reset_index()
        .rename(columns={'level_1': 'State', 0: 'Value'}),
    on=['ID', 'State']
)

print(merged.to_string(index=False))

merged :

ID State  Value
 1    AA      9
 2    AA      7
 3    ZF     88
 3    CJ      6

---

Uses stack to get each value in df2 into its own row:

print(df2.set_index('ID')
        .stack()
        .reset_index()
        .rename(columns={'level_1': 'State', 0: 'Value'}))

Output:

   ID State  Value
0   1    AA      9
1   1    ZF      8
2   1    CJ     77
3   2    AA      7
4   2    ZF      6
5   2    CJ      5
6   3    AA      8
7   3    ZF     88
8   3    CJ      6

Then this easily merges with df1

Answer 3

Here is an option using loc

df1['value'] = df2.set_index('ID').stack().loc[(pd.MultiIndex.from_frame(df1))].to_numpy()

Answer 4

Since you want to map the columns of the second DataFrame to a row in the first DataFrame, you need to first Transpose the second DataFrame, I also suggest removing the 'ID' column for ease:

df2.drop('ID', axis = 1, inplace = True)
df2 = df2.T
df2.columns = ['State', 'Value1', 'Value2', 'Value3']

final_df = pd.merge(df1, df2, on = 'State', how = 'left')