如何根据熊猫中的条件映射两行不同的数据框答案

【问题标题】：how to map two rows of different dataframe based on a condition in pandas如何根据熊猫中的条件映射两行不同的数据框
【发布时间】：2018-08-07 22:29:08
【问题描述】：

我有两个数据框，

df1,

 Names
 one two three
 Sri is a good player
 Ravi is a mentor
 Kumar is a cricketer player

df2,

 values
 sri
 NaN
 sri, is
 kumar,cricketer player

我正在尝试获取 df1 中包含 df2 中所有项目的行

我的预期输出是，

 values                  Names
 sri                     Sri is a good player
 NaN
 sri, is                 Sri is a good player
 kumar,cricketer player  Kumar is a cricketer player

我试过了，df1["Names"].str.contains("|".join(df2["values"].values.tolist())) 我也试过了，

但我无法达到预期的输出，因为它有 (",")。请帮忙

【问题讨论】：

标签： python pandas dataframe data-analysis

【解决方案1】：

在 Numpy 广播中使用集合逻辑。

d1 = df1['Names'].fillna('').str.lower().str.split('[^a-z]+').apply(set).values
d2 = df2['values'].fillna('').str.lower().str.split('[^a-z]+').apply(set).values

i, j = np.where(d1 >= d2[:, None])

df2.assign(Names=pd.Series(df1['Names'].values[j], df2['values'].index[i]))

                   values                        Names
0                     sri         Sri is a good player
1                     NaN                          NaN
2                 sri, is         Sri is a good player
3  kumar,cricketer player  Kumar is a cricketer player

【讨论】：

【解决方案2】：

试试 -

import pandas as pd

df1 = pd.read_csv('sample.csv')
df2 = pd.read_csv('sample_2.csv')

df2['values']= df2['values'].str.lower()
df1['names']= df1['names'].str.lower()

df2["values"] = df2['values'].str.replace('[^\w\s]',' ')
df2['values']= df2['values'].replace('\s+', ' ', regex=True)

df1["names"] = df1['names'].str.replace('[^\w\s]',' ')
df1['names']= df1['names'].replace('\s+', ' ', regex=True)

df2['list_values'] = df2['values'].apply(lambda x: str(x).split())
df1['list_names'] = df1['names'].apply(lambda x: str(x).split())

list_names = df1['list_names'].tolist()

def check_names(x, list_names):
    output = ''
    for list_name in list_names:
        if set(list_name) >= set(x):
            output = ' '.join(list_name)
            break
    return output

df2['Names'] = df2['list_values'].apply(lambda x: check_names(x, list_names))
print(df2)

输出

values                        Names
0                     sri         sri is a good player
1                     NaN                             
2                  sri is         sri is a good player
3  kumar cricketer player  kumar is a cricketer player

解释

这是一个模糊匹配问题。所以这是我应用的步骤 -

删除标点符号并拆分以获得df 上的唯一单词
全部小写以进行标准化匹配。
通过将字符串拆分为列表进行转换。
最后通过check_names()函数进行匹配得到想要的输出

【讨论】：