如何将带有字典列表的熊猫列拆分为每个键的单独列

Posted 2023-03-11

【中文标题】如何将带有字典列表的熊猫列拆分为每个键的单独列

【英文标题】：How to split a pandas column with a list of dicts into separate columns for each key

【发布时间】：2021-04-13 17:15:09

【问题描述】：

我正在分析 来自 Facebook 的政治广告，这是由 ProPublica 发布的 dataset here。

这就是我的意思。 我有一整列要分析的目标，但它的格式对于我的技能水平的人来说非常难以访问。

这仅来自 1 个单元格： ["target": "NAge", "segment": "21 and older", "target": "MinAge", "segment": "21", "target": "Retargeting", "segment": "people who may be similar to their customers", "target": "Region", "segment": "the United States"]

还有另一个： ["target": "NAge", "segment": "18 and older", "target": "Location Type", "segment": "HOME", "target": "Interest", "segment": "Hispanic culture", "target": "Interest", "segment": "Republican Party (United States)", "target": "Location Granularity", "segment": "country", "target": "Country", "segment": "the United States", "target": "MinAge", "segment": 18]

我需要做的是将每个“目标”项分开以成为列标签，其中每个对应的“段”成为该列中的可能值。

或者，是否创建一个函数来调用每行中的每个字典键来计算频率？

【问题讨论】：

查看更新以获取计数。

【参考方案1】：

这些列是lists 的dicts。 可以使用pandas.explode() 将list 中的每个dict 移动到单独的列中。 将dicts 的列转换为数据帧，其中键是列标题，值是观察值，方法是使用pandas.json_normalize()、.join() 这回到df。 使用.drop() 删除不需要的列。 如果该列包含作为字符串的 dicts 列表（例如 "[key: value]"），请在 Splitting dictionary/list inside a Pandas Column into Separate Columns 中引用此 solution，并使用： df.col2 = df.col2.apply(literal_eval)，与from ast import literal_eval。

import pandas as pd

# create sample dataframe
df = pd.DataFrame('col1': ['x', 'y'], 'col2': [["target": "NAge", "segment": "21 and older", "target": "MinAge", "segment": "21", "target": "Retargeting", "segment": "people who may be similar to their customers", "target": "Region", "segment": "the United States"], ["target": "NAge", "segment": "18 and older", "target": "Location Type", "segment": "HOME", "target": "Interest", "segment": "Hispanic culture", "target": "Interest", "segment": "Republican Party (United States)", "target": "Location Granularity", "segment": "country", "target": "Country", "segment": "the United States", "target": "MinAge", "segment": 18]])

# display(df)
  col1                                                                                                                                                                                                                                                                                                                                                                                 col2
0    x                                                                                                                                                   ['target': 'NAge', 'segment': '21 and older', 'target': 'MinAge', 'segment': '21', 'target': 'Retargeting', 'segment': 'people who may be similar to their customers', 'target': 'Region', 'segment': 'the United States']
1    y  ['target': 'NAge', 'segment': '18 and older', 'target': 'Location Type', 'segment': 'HOME', 'target': 'Interest', 'segment': 'Hispanic culture', 'target': 'Interest', 'segment': 'Republican Party (United States)', 'target': 'Location Granularity', 'segment': 'country', 'target': 'Country', 'segment': 'the United States', 'target': 'MinAge', 'segment': 18]

# use explode to give each dict in a list a separate row
df = df.explode('col2').reset_index(drop=True)

# normalize the column of dicts, join back to the remaining dataframe columns, and drop the unneeded column
df = df.join(pd.json_normalize(df.col2)).drop(columns=['col2'])

display(df)

   col1                target                                       segment
0     x                  NAge                                  21 and older
1     x                MinAge                                            21
2     x           Retargeting  people who may be similar to their customers
3     x                Region                             the United States
4     y                  NAge                                  18 and older
5     y         Location Type                                          HOME
6     y              Interest                              Hispanic culture
7     y              Interest              Republican Party (United States)
8     y  Location Granularity                                       country
9     y               Country                             the United States
10    y                MinAge                                            18

获取count

如果目标是为每个'target' 和关联的'segment' 获取count

counts = df.groupby(['target', 'segment']).count()

更新

此更新针对完整文件实施

import pandas as pd
from ast import literal_eval

# load the file
df = pd.read_csv('en-US.csv')

# replace NaNs with '[]', otherwise literal_eval will error
df.targets = df.targets.fillna('[]')

# replace null with None, otherwise literal_eval will error
df.targets = df.targets.str.replace('null', 'None')

# convert the strings to lists of dicts
df.targets = df.targets.apply(literal_eval)

# use explode to give each dict in a list a separate row
df = df.explode('targets').reset_index(drop=True)

# fillna with  is required for json_normalize
df.targets = df.targets.fillna(i:  for i in df.index)

# normalize the column of dicts, join back to the remaining dataframe columns, and drop the unneeded column
normalized = pd.json_normalize(df.targets)

# get the counts
counts = normalized.groupby(['target', 'segment']).segment.count().reset_index(name='counts')