pandas 在熊猫数据框中的任何列中删除带有“问号”值的行
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Drop rows with a 'question mark' value in any column in a pandas dataframe
提问by Anonymous
I want to remove all rows (or take all rows without) a question mark symbol in any column. I also want to change the elements to floattype.
我想删除任何列中的所有行（或删除所有行）一个问号符号。我也想将元素更改为浮动类型。
Input:
输入：
X Y Z
0 1 ?
1 2 3
? ? 4
4 4 4
? 2 5
Output:
输出：
X Y Z
1 2 3
4 4 4
Preferably using pandas dataframe operations.
最好使用Pandas数据框操作。
回答by jezrael
You can try first find string ?in columns, create boolean mask and last filter rows - use boolean indexing. If you need convert columns to float, use astype:
您可以尝试首先?在列中查找字符串，创建布尔掩码并最后过滤行 - 使用布尔索引。如果您需要将列转换为float，请使用astype：
print ~((df['X'] == '?' ) (df['Y'] == '?' ) | (df['Z'] == '?' ))
0 False
1 True
2 False
3 True
4 False
dtype: bool
df1 = df[~((df['X'] == '?' ) | (df['Y'] == '?' ) | (df['Z'] == '?' ))].astype(float)
print df1
X Y Z
1 1 2 3
3 4 4 4
print df1.dtypes
X float64
Y float64
Z float64
dtype: object
Or you can try:
或者你可以试试：
df['X'] = pd.to_numeric(df['X'], errors='coerce')
df['Y'] = pd.to_numeric(df['Y'], errors='coerce')
df['Z'] = pd.to_numeric(df['Z'], errors='coerce')
print df
X Y Z
0 0 1 NaN
1 1 2 3
2 NaN NaN 4
3 4 4 4
4 NaN 2 5
print ((df['X'].notnull() ) & (df['Y'].notnull() ) & (df['Z'].notnull() ))
0 False
1 True
2 False
3 True
4 False
dtype: bool
print df[ ((df['X'].notnull() ) & (df['Y'].notnull() ) & (df['Z'].notnull() )) ].astype(float)
X Y Z
1 1 2 3
3 4 4 4
Better is use:
更好的是使用：
df = df[(df != '?').all(axis=1)]
Or:
或者：
df = df[~(df == '?').any(axis=1)]
回答by Naidu Jithendra
You can try replacing ?with null values
您可以尝试用?空值替换
import numpy as np
data = df.replace("?", "np.Nan")
if you want to replace particular column try this:
如果要替换特定列，请尝试以下操作：
data = df["column name"].replace("?", "np.Nan")
