从 Pandas 数据框中删除带有空列表的行
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Remove rows with empty lists from pandas data frame
提问by Ben Price
I have a data frame with some columns with empty lists and others with lists of strings:
我有一个数据框，其中一些列带有空列表，而另一些列带有字符串列表：
donation_orgs donation_context
0 [] []
1 [the research of Dr. ...] [In lieu of flowers , memorial donations ...]
I'm trying to return a data set without any of the rows where there are empty lists.
我正在尝试返回一个没有任何空列表行的数据集。
I've tried just checking for null values:
我试过只检查空值：
dfnotnull = df[df.donation_orgs != []]
dfnotnull
and
和
dfnotnull = df[df.notnull().any(axis=1)]
pd.options.display.max_rows=500
dfnotnull
And I've tried looping through and checking for values that exist, but I think the lists aren't returning Null or None like I thought they would:
我已经尝试遍历并检查存在的值，但我认为列表不会像我想象的那样返回 Null 或 None ：
dfnotnull = pd.DataFrame(columns=('donation_orgs', 'donation_context'))
for i in range(0,len(df)):
if df['donation_orgs'].iloc(i):
dfnotnull.loc[i] = df.iloc[i]
All three of the above methods simply return every row in the original data frame.=
以上三种方法都简单地返回原始数据框中的每一行。=
回答by Victor
To avoid converting to strand actually use the lists, you can do this:
为避免转换为str并实际使用lists，您可以这样做：
df[df['donation_orgs'].map(lambda d: len(d)) > 0]
It maps the donation_orgscolumn to the length of the listsof each row and keeps only the ones that have at least one element, filtering out empty lists.
它将donation_orgs列映射到每一行的列表的长度，并只保留至少具有一个 element 的那些，过滤掉空列表。
It returns
它返回
Out[1]:
donation_context donation_orgs
1 [In lieu of flowers , memorial donations] [the research of Dr.]
as expected.
正如预期的那样。
回答by Woody Pride
You could try slicing as though the data frame were strings instead of lists:
您可以尝试切片，就好像数据框是字符串而不是列表：
import pandas as pd
df = pd.DataFrame({
'donation_orgs' : [[], ['the research of Dr.']],
'donation_context': [[], ['In lieu of flowers , memorial donations']]})
df[df.astype(str)['donation_orgs'] != '[]']
Out[9]:
donation_context donation_orgs
1 [In lieu of flowers , memorial donations] [the research of Dr.]
回答by Amir Imani
You can use the following one-liner:
您可以使用以下单行：
df[(df['donation_orgs'].str.len() != 0) | (df['donation_context'].str.len() != 0)]
回答by Mark
Assuming that you read data from a CSV, the other possible solution could be this:
假设您从 CSV 读取数据，另一种可能的解决方案可能是：
import pandas as pd
df = pd.read_csv('data.csv', na_filter=True, na_values='[]')
df.dropna()
na_filterdefines additional string to recognize as NaN. I tested this on pandas-0.24.2.
na_filter定义附加字符串以识别为 NaN。我在pandas-0.24.2.
