How to replace NaN values by Zeroes in a column of a Pandas Dataframe?
PythonPandasDataframeNanPython Problem Overview
I have a Pandas Dataframe as below:
itm Date Amount
67 420 2012-09-30 00:00:00 65211
68 421 2012-09-09 00:00:00 29424
69 421 2012-09-16 00:00:00 29877
70 421 2012-09-23 00:00:00 30990
71 421 2012-09-30 00:00:00 61303
72 485 2012-09-09 00:00:00 71781
73 485 2012-09-16 00:00:00 NaN
74 485 2012-09-23 00:00:00 11072
75 485 2012-09-30 00:00:00 113702
76 489 2012-09-09 00:00:00 64731
77 489 2012-09-16 00:00:00 NaN
When I try to apply a function to the Amount column, I get the following error:
ValueError: cannot convert float NaN to integer
I have tried applying a function using .isnan from the Math Module I have tried the pandas .replace attribute I tried the .sparse data attribute from pandas 0.9 I have also tried if NaN == NaN statement in a function. I have also looked at this article https://stackoverflow.com/questions/8161836/how-do-i-replace-na-values-with-zeros-in-r whilst looking at some other articles. All the methods I have tried have not worked or do not recognise NaN. Any Hints or solutions would be appreciated.
Python Solutions
Solution 1 - Python
I believe DataFrame.fillna() will do this for you.
Link to Docs for a dataframe and for a Series.
Example:
In [7]: df
Out[7]:
0 1
0 NaN NaN
1 -0.494375 0.570994
2 NaN NaN
3 1.876360 -0.229738
4 NaN NaN
In [8]: df.fillna(0)
Out[8]:
0 1
0 0.000000 0.000000
1 -0.494375 0.570994
2 0.000000 0.000000
3 1.876360 -0.229738
4 0.000000 0.000000
To fill the NaNs in only one column, select just that column. in this case I'm using inplace=True to actually change the contents of df.
In [12]: df[1].fillna(0, inplace=True)
Out[12]:
0 0.000000
1 0.570994
2 0.000000
3 -0.229738
4 0.000000
Name: 1
In [13]: df
Out[13]:
0 1
0 NaN 0.000000
1 -0.494375 0.570994
2 NaN 0.000000
3 1.876360 -0.229738
4 NaN 0.000000
EDIT:
To avoid a SettingWithCopyWarning, use the built in column-specific functionality:
df.fillna({1:0}, inplace=True)
Solution 2 - Python
It is not guaranteed that the slicing returns a view or a copy. You can do
df['column'] = df['column'].fillna(value)
Solution 3 - Python
You could use replace to change NaN to 0:
import pandas as pd
import numpy as np
# for column
df['column'] = df['column'].replace(np.nan, 0)
# for whole dataframe
df = df.replace(np.nan, 0)
# inplace
df.replace(np.nan, 0, inplace=True)
Solution 4 - Python
The below code worked for me.
import pandas
df = pandas.read_csv('somefile.txt')
df = df.fillna(0)
Solution 5 - Python
I just wanted to provide a bit of an update/special case since it looks like people still come here. If you're using a multi-index or otherwise using an index-slicer the inplace=True option may not be enough to update the slice you've chosen. For example in a 2x2 level multi-index this will not change any values (as of pandas 0.15):
idx = pd.IndexSlice
df.loc[idx[:,mask_1],idx[mask_2,:]].fillna(value=0,inplace=True)
The "problem" is that the chaining breaks the fillna ability to update the original dataframe. I put "problem" in quotes because there are good reasons for the design decisions that led to not interpreting through these chains in certain situations. Also, this is a complex example (though I really ran into it), but the same may apply to fewer levels of indexes depending on how you slice.
The solution is DataFrame.update:
df.update(df.loc[idx[:,mask_1],idx[[mask_2],:]].fillna(value=0))
It's one line, reads reasonably well (sort of) and eliminates any unnecessary messing with intermediate variables or loops while allowing you to apply fillna to any multi-level slice you like!
If anybody can find places this doesn't work please post in the comments, I've been messing with it and looking at the source and it seems to solve at least my multi-index slice problems.
Solution 6 - Python
You can also use dictionaries to fill NaN values of the specific columns in the DataFrame rather to fill all the DF with some oneValue.
import pandas as pd
df = pd.read_excel('example.xlsx')
df.fillna( {
'column1': 'Write your values here',
'column2': 'Write your values here',
'column3': 'Write your values here',
'column4': 'Write your values here',
.
.
.
'column-n': 'Write your values here'} , inplace=True)
Solution 7 - Python
Easy way to fill the missing values:-
filling string columns: when string columns have missing values and NaN values.
df['string column name'].fillna(df['string column name'].mode().values[0], inplace = True)
filling numeric columns: when the numeric columns have missing values and NaN values.
df['numeric column name'].fillna(df['numeric column name'].mean(), inplace = True)
filling NaN with zero:
df['column name'].fillna(0, inplace = True)
Solution 8 - Python
To replace na values in pandas
df['column_name'].fillna(value_to_be_replaced,inplace=True)
if inplace = False, instead of updating the df (dataframe) it will return the modified values.
Solution 9 - Python
Considering the particular column Amount in the above table is of integer type. The following would be a solution :
df['Amount'] = df.Amount.fillna(0).astype(int)
Similarly, you can fill it with various data types like float, str and so on.
In particular, I would consider datatype to compare various values of the same column.
Solution 10 - Python
To replace nan in different columns with different ways:
replacement= {'column_A': 0, 'column_B': -999, 'column_C': -99999}
df.fillna(value=replacement)
Solution 11 - Python
Replace all nan with 0
df = df.fillna(0)
Solution 12 - Python
There have been many contributions already, but since I'm new here, I will still give input.
There are two approaches to replace NaN values with zeros in Pandas DataFrame:
- fillna(): function fills NA/NaN values using the specified method.
- replace(): df.replace()a simple method used to replace a string, regex, list, dictionary
Example:
#NaN with zero on all columns
df2 = df.fillna(0)
#Using the inplace=True keyword in a pandas method changes the default behaviour.
df.fillna(0, inplace = True)
# multiple columns appraoch
df[["Student", "ID"]] = df[["Student", "ID"]].fillna(0)
finally the replace() method :
df["Student"] = df["Student"].replace(np.nan, 0)
Solution 13 - Python
This works for me, but no one's mentioned it. could there be something wrong with it?
df.loc[df['column_name'].isnull(), 'column_name'] = 0
Solution 14 - Python
If you were to convert it to a pandas dataframe, you can also accomplish this by using fillna.
import numpy as np
df=np.array([[1,2,3, np.nan]])
import pandas as pd
df=pd.DataFrame(df)
df.fillna(0)
This will return the following:
0 1 2 3
0 1.0 2.0 3.0 NaN
>>> df.fillna(0)
0 1 2 3
0 1.0 2.0 3.0 0.0
Solution 15 - Python
There are two options available primarily; in case of imputation or filling of missing values NaN / np.nan with only numerical replacements (across column(s):
df['Amount'].fillna(value=None, method= ,axis=1,) is sufficient:
From the Documentation:
value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list.
Which means 'strings' or 'constants' are no longer permissable to be imputed.
For more specialized imputations use SimpleImputer():
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='constant', missing_values=np.nan, fill_value='Replacement_Value')
df[['Col-1', 'Col-2']] = si.fit_transform(X=df[['C-1', 'C-2']])
Solution 16 - Python
If you want to fill NaN for a specific column you can use loc:
d1 = {"Col1" : ['A', 'B', 'C'],
"fruits": ['Avocado', 'Banana', 'NaN']}
d1= pd.DataFrame(d1)
output:
Col1 fruits
0 A Avocado
1 B Banana
2 C NaN
d1.loc[ d1.Col1=='C', 'fruits' ] = 'Carrot'
output:
Col1 fruits
0 A Avocado
1 B Banana
2 C Carrot
Solution 17 - Python
I think it's also worth mention and explain the parameters configuration of fillna() like Method, Axis, Limit, etc.
From the documentation we have:
Series.fillna(value=None, method=None, axis=None,
inplace=False, limit=None, downcast=None)
Fill NA/NaN values using the specified method.
Parameters
value [scalar, dict, Series, or DataFrame] Value to use to
fill holes (e.g. 0), alternately a dict/Series/DataFrame
of values specifying which value to use for each index
(for a Series) or column (for a DataFrame). Values not in
the dict/Series/DataFrame will not be filled. This
value cannot be a list.
method [{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None},
default None] Method to use for filling holes in
reindexed Series pad / ffill: propagate last valid
observation forward to next valid backfill / bfill:
use next valid observation to fill gap axis
[{0 or ‘index’}] Axis along which to fill missing values.
inplace [bool, default False] If True, fill
in-place. Note: this will modify any other views
on this object (e.g., a no-copy slice for a
column in a DataFrame).
limit [int,defaultNone] If method is specified,
this is the maximum number of consecutive NaN
values to forward/backward fill. In other words,
if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled.
If method is not specified, this is the maximum
number of entries along the entire axis where NaNs
will be filled. Must be greater than 0 if not None.
downcast [dict, default is None] A dict of item->dtype
of what to downcast if possible, or the string ‘infer’
which will try to downcast to an appropriate equal
type (e.g. float64 to int64 if possible).
Ok. Let's start with the method= Parameter this
have forward fill (ffill) and backward fill(bfill)
ffill is doing copying forward the previous
non missing value.
e.g. :
import pandas as pd
import numpy as np
inp = [{'c1':10, 'c2':np.nan, 'c3':200}, {'c1':np.nan,'c2':110, 'c3':210}, {'c1':12,'c2':np.nan, 'c3':220},{'c1':12,'c2':130, 'c3':np.nan},{'c1':12,'c2':np.nan, 'c3':240}]
df = pd.DataFrame(inp)
c1 c2 c3
0 10.0 NaN 200.0
1 NaN 110.0 210.0
2 12.0 NaN 220.0
3 12.0 130.0 NaN
4 12.0 NaN 240.0
Forward fill:
df.fillna(method="ffill")
c1 c2 c3
0 10.0 NaN 200.0
1 10.0 110.0 210.0
2 12.0 110.0 220.0
3 12.0 130.0 220.0
4 12.0 130.0 240.0
Backward fill:
df.fillna(method="bfill")
c1 c2 c3
0 10.0 110.0 200.0
1 12.0 110.0 210.0
2 12.0 130.0 220.0
3 12.0 130.0 240.0
4 12.0 NaN 240.0
The Axis Parameter help us to choose the direction of the fill:
Fill directions:
ffill:
Axis = 1
Method = 'ffill'
----------->
direction
df.fillna(method="ffill", axis=1)
c1 c2 c3
0 10.0 10.0 200.0
1 NaN 110.0 210.0
2 12.0 12.0 220.0
3 12.0 130.0 130.0
4 12.0 12.0 240.0
Axis = 0 # by default
Method = 'ffill'
|
| # direction
|
V
e.g: # This is the ffill default
df.fillna(method="ffill", axis=0)
c1 c2 c3
0 10.0 NaN 200.0
1 10.0 110.0 210.0
2 12.0 110.0 220.0
3 12.0 130.0 220.0
4 12.0 130.0 240.0
bfill:
axis= 0
method = 'bfill'
^
|
|
|
df.fillna(method="bfill", axis=0)
c1 c2 c3
0 10.0 110.0 200.0
1 12.0 110.0 210.0
2 12.0 130.0 220.0
3 12.0 130.0 240.0
4 12.0 NaN 240.0
axis = 1
method = 'bfill'
<-----------
df.fillna(method="bfill", axis=1)
c1 c2 c3
0 10.0 200.0 200.0
1 110.0 110.0 210.0
2 12.0 220.0 220.0
3 12.0 130.0 NaN
4 12.0 240.0 240.0
# alias:
# 'fill' == 'pad'
# bfill == backfill
limit parameter:
df
c1 c2 c3
0 10.0 NaN 200.0
1 NaN 110.0 210.0
2 12.0 NaN 220.0
3 12.0 130.0 NaN
4 12.0 NaN 240.0
Only replace the first NaN element across columns:
df.fillna(value = 'Unavailable', limit=1)
c1 c2 c3
0 10.0 Unavailable 200.0
1 Unavailable 110.0 210.0
2 12.0 NaN 220.0
3 12.0 130.0 Unavailable
4 12.0 NaN 240.0
df.fillna(value = 'Unavailable', limit=2)
c1 c2 c3
0 10.0 Unavailable 200.0
1 Unavailable 110.0 210.0
2 12.0 Unavailable 220.0
3 12.0 130.0 Unavailable
4 12.0 NaN 240.0
downcast parameter:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 c1 4 non-null float64
1 c2 2 non-null float64
2 c3 4 non-null float64
dtypes: float64(3)
memory usage: 248.0 bytes
df.fillna(method="ffill",downcast='infer').info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 c1 5 non-null int64
1 c2 4 non-null float64
2 c3 5 non-null int64
dtypes: float64(1), int64(2)
memory usage: 248.0 bytes