根据数据框中另一列的值为列表中的数据框列分配值

Question

Assigning values to dataframe columns from a list based on value of another column in dataframe

I have two dataframes, df1 and df2, where

df1 = pd.DataFrame(np.array([['a', 'b', 'c','d'], [1, 2, 3, 4]]),
                   columns=['x', 'y'])


df2 = pd.DataFrame(np.array([['a', 'b', 'c', 'a', 'c', 'b','b'], [4, 5, 6, 1, 32, 1, 8]]),
                   columns=['x', 'z'])

I want to create a dataframe df3 based on df2 with the appropriate values of df1['y'] assigned. So for example I would like the result to look like:

df3 = pd.DataFrame(np.array([['a', 'b', 'c', 'a', 'c', 'b','b'], [4, 5, 6, 1, 32, 1, 8] , [1, 2, 3, 1, 3, 2, 2 ] ]),
                   columns=['x', 'z', 'y'])

In my real case there are many thousand possible values of x so I would like to avoid if df3['x'] == a: df3['y'] = 1 style solutions if possible.

Answer 1

您可以通过合并实现此目的：

df3 = df2.merge(df1, how="left", on="x")

Answer 2

Setup:

import pandas as pd
import numpy as np

data={'x':['a', 'b', 'c','d'],
      'y':[1, 2, 3, 4]}
df1 = pd.DataFrame(data)

data2={'x':['a', 'b', 'c', 'a', 'c', 'b','b'],
       'z':[4, 5, 6, 1, 32, 1, 8]}
df2 = pd.DataFrame(data2)

data3={'x':['a', 'b', 'c', 'a', 'c', 'b','b'],
       'z':[4, 5, 6, 1, 32, 1, 8]}
df3 = pd.DataFrame(data3)

Dictionary method:

# Make a dictionary
dict = df1.set_index('x').to_dict()
# Map to dictionary
df3 = df2.assign(x=df2['x'].map(*dict.values()))

You have to unzip dict because it actually comes out as 2 dicts, but you only want the second here.