Python熊猫:基于图例数据框标记分类值

您可以使用从 df2型 . 这样地:

首先,生成一些虚拟数据:

import pandas as pd
import numpy as np

df1 = pd.DataFrame()
df1['obs'] = range(1,1001)
df1['gender'] = np.random.choice([1,2],1000)
df1['job'] = np.random.choice([1,2],1000)

df2 = pd.DataFrame()
df2['var'] = ['gender','gender','job','job']
df2['value'] = [1,2,1,2]
df2['label'] = ['male','female','blue collar', 'white collar']

如果要替换一个类似以下内容的变量:

genderDict = dict(df2.loc[df2['var']=='gender'][['value','label']].values)
df1['gender_name'] = df1['gender'].apply(lambda x: genderDict[x])

如果要替换一组变量:

colNames = list(df1.columns)
colNames.remove('obs')
for variable in colNames:
    varDict = dict(df2.loc[df2['var']==variable][['value','label']].values)
    df1[variable+'_name'] = df1[variable].apply(lambda x: varDict[x])

对于一百万行,大约需要1秒的时间,因此速度应该合理。

使用groupby从df2创建映射器字典

d = df2.groupby('Var').apply(lambda x: dict(zip(x['Value'], x['Label']))).to_dict()

{'gender': {1: 'male', 2: 'female'},
'job': {1: 'blue collar', 2: 'white collar'}}

现在,使用字典的外键作为列映射df1中的值,而内部字典是映射器

for col in df1.columns:
    if col in d.keys():
        df1[col] = df1[col].map(d[col])

你得到

    obs gender  job
0   1   male    blue collar
1   2   male    white collar
2   3   female  white collar
3   4   male    blue collar