In [7]: df
Out[7]:010 NaN NaN
1-0.4943750.5709942 NaN NaN
31.876360-0.2297384 NaN NaN
In [8]: df.fillna(0)
Out[8]:0100.0000000.0000001-0.4943750.57099420.0000000.00000031.876360-0.22973840.0000000.000000
In [1]: df = DataFrame([[True,True,False],[False,False,True]]).T
In [2]: df
Out[2]:010TrueFalse1TrueFalse2FalseTrue
In [3]: df.applymap(lambda x:1if x else np.nan)
Out[3]:0101 NaN
11 NaN
2 NaN 1
replace all occurrences of a value to nan in pandas
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1':['one','two','three','four']})
df['col1']= df['col1'].map(lambda x: np.nan if x in['two','four']else x)
from ast import literal_eval
from io import StringIO
# replicate csv file
x = StringIO("""A,B
,"('t1', 't2')"
"('t3', 't4')",""")defliteral_converter(val):# replace first val with '' or some other null identifier if requiredreturn val if val ==''else literal_eval(val)
df = pd.read_csv(x, delimiter=',', converters=dict.fromkeys('AB', literal_converter))print(df)
A B
0(t1, t2)1(t3, t4)