Drop rows with a 'question mark' value in any column in a pandas dataframe
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
:
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:
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:
df = df[(df != '?').all(axis=1)]
Or:
df = df[~(df == '?').any(axis=1)]
You can try replacing ?
with null values
import numpy as np
data = df.replace("?", "np.Nan")
if you want to replace particular column try this:
data = df["column name"].replace("?", "np.Nan")