CODEWITHSUNDEEP
pythoncsvpandas
lollercoaster
lollercoaster

Reputation: 16503

removing NaN values in python pandas

Data is of income of adults from census data, rows look like:

31, Private, 84154, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 38, NaN, >50K
48, Self-emp-not-inc, 265477, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K

I'm trying to remove all rows with NaNs from a DataFrame loaded from a CSV file in pandas.

>>> import pandas as pd
>>> income = pd.read_csv('income.data')
>>> income['type'].unique()
array([ State-gov,  Self-emp-not-inc,  Private,  Federal-gov,  Local-gov,
    NaN,  Self-emp-inc,  Without-pay,  Never-worked], dtype=object)
>>> income.dropna(how='any') # should drop all rows with NaNs
>>> income['type'].unique()
array([ State-gov,  Self-emp-not-inc,  Private,  Federal-gov,  Local-gov,
    NaN,  Self-emp-inc,  Without-pay,  Never-worked], dtype=object)
    Self-emp-inc, nan], dtype=object) # what??
>>> income = income.dropna(how='any') # ok, maybe reassignment will work?
>>> income['type'].unique()
array([ State-gov,  Self-emp-not-inc,  Private,  Federal-gov,  Local-gov,
    NaN,  Self-emp-inc,  Without-pay,  Never-worked], dtype=object) # what??

I tried with a smaller example.csv:

label,age,sex
1,43,M
-1,NaN,F
1,65,NaN

And dropna() worked just fine here for both categorical and numerical NaNs. What is going on? I'm new to Pandas, just learning the ropes.

Upvotes: 4

Views: 29847

Answers (2)

Francis Odero
Francis Odero

Reputation: 141

Drop all rows with NaN values

df2=df.dropna()
df2=df.dropna(axis=0)

Reset index after drop

df2=df.dropna().reset_index(drop=True)

Drop row that has all NaN values

df2=df.dropna(how='all')

Drop rows that has NaN values on selected columns

df2=df.dropna(subset=['length','Height'])

Upvotes: 2

dorvak
dorvak

Reputation: 9709

As I wrote in the comment: The "NaN" has a leading whitespace (at least in the data you provided). Therefore, you need to specifiy the na_values paramter in the read_csv function.

Try this one:

df = pd.read_csv("income.csv",header=None,na_values=" NaN")

This is why your second example works, because there is no leading whitespace here.

Upvotes: 8

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