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我正在分析来自 Facebook 的政治广告，这是ProPublica在此处发布的数据集。

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

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"}]

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”项目分开以成为列标签，其中每个相应的“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？

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1 回答 1

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  • 列lists是dicts。
    • 可以使用 将dict中的每个移动到单独的列。listpandas.explode()
    • 将 的列转换为dicts数据框，其中键是列标题，值是观察值，使用pandas.json_normalize(),.join()这回到df.
  • 用于.drop()删除不需要的列。
  • 如果该列包含作为字符串的字典列表（例如"[{key: value}]"），请参阅将Pandas 列内的字典/列表拆分为单独的列中的此解决方案，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

得到count

  • 如果目标是获取count每个'target'相关的'segment'
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')
于 2021-01-08T00:09:51.820 回答