Python 熊猫数据框中的字典列
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Dictionary column in pandas dataframe
提问by user1274037
I've got a csv that I'm reading into a pandas dataframe. However one of the columns is in the form of a dictionary. Here is an example:
我有一个 csv，我正在读入一个 Pandas 数据框。然而，其中一列是字典的形式。下面是一个例子：
ColA, ColB, ColC, ColdD
20, 30, {"ab":"1", "we":"2", "as":"3"},"String"
How can I turn this into a dataframe that looks like this:
我怎样才能把它变成一个看起来像这样的数据框：
ColA, ColB, AB, WE, AS, ColdD
20, 30, "1", "2", "3", "String"
editI fixed up the question, it looks like this but is a string that needs to be parsed, not dict object.
编辑我解决了这个问题，它看起来像这样，但它是一个需要解析的字符串，而不是 dict 对象。
回答by jedwards
What about something like:
怎么样：
import pandas as pd
# Create mock dataframe
df = pd.DataFrame([
[20, 30, {'ab':1, 'we':2, 'as':3}, 'String1'],
[21, 31, {'ab':4, 'we':5, 'as':6}, 'String2'],
[22, 32, {'ab':7, 'we':8, 'as':9}, 'String2'],
], columns=['Col A', 'Col B', 'Col C', 'Col D'])
# Create dataframe where you'll store the dictionary values
ddf = pd.DataFrame(columns=['AB','WE','AS'])
# Populate ddf dataframe
for (i,r) in df.iterrows():
e = r['Col C']
ddf.loc[i] = [e['ab'], e['we'], e['as']]
# Replace df with the output of concat(df, ddf)
df = pd.concat([df, ddf], axis=1)
# New column order, also drops old Col C column
df = df[['Col A', 'Col B', 'AB', 'WE', 'AS', 'Col D']]
print(df)
Output:
输出：
Col A Col B AB WE AS Col D 0 20 30 1 2 3 String1 1 21 31 4 5 6 String2 2 22 32 7 8 9 String2
回答by Bob Haffner
So starting with your one row df
所以从你的一行 df 开始
Col A Col B Col C Col D
0 20 30 {u'we': 2, u'ab': 1, u'as': 3} String1
EDIT: based on the comment by the OP, I'm assuming we need to convert the string first
编辑：根据 OP 的评论，我假设我们需要先转换字符串
import ast
df["ColC"] = df["ColC"].map(lambda d : ast.literal_eval(d))
then we convert Col C to a dict, transpose it and then join it to the original df
然后我们将 Col C 转换为 dict，将其转置，然后将其加入原始 df
dfNew = df.join(pd.DataFrame(df["Col C"].to_dict()).T)
dfNew
which gives you this
这给了你这个
Col A Col B Col C Col D ab as we
0 20 30 {u'we': 2, u'ab': 1, u'as': 3} String1 1 3 2
Then we just select the columns we want in dfNew
然后我们只需要在dfNew中选择我们想要的列
dfNew[["Col A", "Col B", "ab", "we", "as", "Col D"]]
Col A Col B ab we as Col D
0 20 30 1 2 3 String1
回答by psychemedia
As per https://stackoverflow.com/a/38231651/454773, you can use .apply(pd.Series)to map the dict containing column onto new columns and then concatenate these new columns back into the original dataframe minus the original dict containing column:
根据https://stackoverflow.com/a/38231651/454773，您可以使用.apply(pd.Series)将包含 dict 的列映射到新列，然后将这些新列连接回原始数据帧减去原始 dict 包含列：
dw=pd.DataFrame( [[20, 30, {"ab":"1", "we":"2", "as":"3"},"String"]],
columns=['ColA', 'ColB', 'ColC', 'ColdD'])
pd.concat([dw.drop(['ColC'], axis=1), dw['ColC'].apply(pd.Series)], axis=1)
Returns:
返回：
ColA ColB ColdD ab as we
20 30 String 1 3 2
