时间：2020-12-18 02:23:14 | 栏目：Python代码 | 点击：次
在使用 pandas 进行数据分析的过程中，我们常常会遇到将一行数据展开成多行的需求，多么希望能有一个类似于 hive sql 中的 explode 函数。
这个函数如下：
Code
# !/usr/bin/env python
# -*- coding:utf-8 -*-
# create on 18/4/13
import pandas as pd
def dataframe_explode(dataframe, fieldname):
temp_fieldname = fieldname + '_made_tuple_'
dataframe[temp_fieldname] = dataframe[fieldname].apply(tuple)
list_of_dataframes = []
for values in dataframe[temp_fieldname].unique().tolist():
list_of_dataframes.append(pd.DataFrame({
temp_fieldname: [values] * len(values),
fieldname: list(values),
}))
dataframe = dataframe[list(set(dataframe.columns) - set([fieldname]))].merge(pd.concat(list_of_dataframes), how='left', on=temp_fieldname)
del dataframe[temp_fieldname]
return dataframe
df = pd.DataFrame({'listcol':[[1,2,3],[4,5,6]], "aa": [222,333]})
df = dataframe_explode(df, "listcol")
Description
将 dataframe 按照某一指定列进行展开，使得原来的每一行展开成一行或多行。( 注：该列可迭代， 例如list, tuple, set)
补充知识：Pandas列中的字典/列表拆分为单独的列
我就废话不多说了，大家还是直接看代码吧
[1] df
Station ID Pollutants
8809 {"a": "46", "b": "3", "c": "12"}
8810 {"a": "36", "b": "5", "c": "8"}
8811 {"b": "2", "c": "7"}
8812 {"c": "11"}
8813 {"a": "82", "c": "15"}
Method 1：
step 1: convert the Pollutants column to Pandas dataframe series
df_pol_ps = data_df['Pollutants'].apply(pd.Series) df_pol_ps: a b c 0 46 3 12 1 36 5 8 2 NaN 2 7 3 NaN NaN 11 4 82 NaN 15
step 2: concat columns a, b, c and drop/remove the Pollutants
df_final = pd.concat([df, df_pol_ps], axis = 1).drop('Pollutants', axis = 1)
df_final:
StationID a b c
0 8809 46 3 12
1 8810 36 5 8
2 8811 NaN 2 7
3 8812 NaN NaN 11
4 8813 82 NaN 15
Method 2：
df_final = pd.concat([df, df['Pollutants'].apply(pd.Series)], axis = 1).drop('Pollutants', axis = 1)
df_final:
StationID a b c
0 8809 46 3 12
1 8810 36 5 8
2 8811 NaN 2 7
3 8812 NaN NaN 11
4 8813 82 NaN 15