I'm currently having a problem with filling the missing values of my dataframe using a different dataframe.
Data samples:
df1
A B C
b 1.0 1.0
d NaN NaN
c 2.0 2.0
a NaN NaN
f NaN NaN
df2
A B C
c 1 5
b 2 6
a 3 7
d 4 8
I've tried to follow the solution in this question but it would appear that it is only possible if the values you're looking up is present in both dataframes you're joining.
My attempt
mask = df1["B"].isnull()
df1.loc[mask, "B"] = df2[df1.loc[mask, "A"]].values
Error:
"None of [Index(['d', 'a', 'f'], dtype='object')] are in the [columns]"
Expected result:
A B C
b 1.0 1.0
d 4.0 8.0
c 2.0 2.0
a 3.0 7.0
f NaN NaN
Also, can it be used it fill two columns?
You can use combine_first here, which is exactly aimed at filling NaNs by matching with another dataframe's columns:
df1.set_index('A').combine_first(df2.set_index('A')).reset_index()
A B C
0 a 3.0 7.0
1 b 1.0 1.0
2 c 2.0 2.0
3 d 4.0 8.0
4 f NaN NaN
Related
Say I have two data frames:
Original:
A B C
0 NaN 4.0 7.0
1 2.0 5.0 NaN
2 NaN NaN 9.0
Imputation:
A B C
0 1 4 7
1 2 5 8
2 3 6 9
(both are the same dataframes except imputation has the NaN's filled in).
I would like to reintroduce the NaN values into the imputation df column A so it looks like this(column B, C are filled in but A keeps the NaN values):
# A B C
# 0 NaN 4.0 7.0
# 1 2.0 5.0 8.0
# 2 NaN 6.0 9.0
import pandas as pd
import numpy as np
dfImputation = pd.DataFrame({'A':[1,2,3],
'B':[4,5,6],
'C':[7,8,9]})
dfOrginal = pd.DataFrame({'A':[np.NaN,2,np.NaN],
'B':[4,5,np.NaN],
'C':[7,np.NaN,9]})
print(dfOrginal.fillna(dfImputation))
I do not get the result I want because it just obviously fills in all values. There is a way to introduce NaN values or a way to fill in NA for specific columns? I'm not quite sure the best approach to get the intended outcome.
You can fill in only specified columns by subsetting the frame you pass into the fillna operation:
>>> dfOrginal.fillna(dfImputation[["B", "C"]])
A B C
0 NaN 4.0 7.0
1 2.0 5.0 8.0
2 NaN 6.0 9.0
Check update
df.update(im[['B','C']])
df
Out[7]:
A B C
0 NaN 4.0 7.0
1 2.0 5.0 8.0
2 NaN 6.0 9.0
How do you fill only groups inside a dataframe which are not fully nulls?
In the dataframe below, only groups with df.A=b and df.A=c should get filled.
df
A B
0 a NaN
1 a NaN
2 a NaN
3 a NaN
4 b 4.0
5 b NaN
6 b 6.0
7 b 6.0
8 c 7.0
9 c NaN
10 c NaN
Was thinking something like:
if set(df[df.A==(need help here)].B.values) == {np.nan}:.
We can do groupby
df.B=df.groupby('A').B.apply(lambda x : x.ffill().bfill())
Get the indices that are not completely null, and then forwardfill/backwardfill on these indices
df = df.set_index("A")
#get index where entries in B are not completely full
ind = df.loc[df.groupby("A").B.transform(lambda x: x.eq(x))].index.unique()
df.loc[ind] = df.loc[ind].ffill().bfill()
print(df)
B
A
a NaN
a NaN
a NaN
a NaN
b 4.0
b 4.0
b 6.0
b 6.0
c 7.0
c 7.0
c 7.0
I have a dataframe containing values as well as some NaN. Now I have the mean of the columns and I want to insert the mean of the particular column into the NaN values. For eg:
ColA and ColB have NaN to be replaced with the value of mean I have
I have the mean for ColA and ColB. I want to insert them into the NaN locations. I could do that individually using the replace method. But for many columns, is there any other way to achieve this?
EDIT:
If already has Series with means only use DataFrame.fillna:
df = pd.DataFrame({
'A':list('abcdef'),
'B':[4,np.nan,4,5,5,4],
'C':[7,8,9,4,2,3],
'D':[1,3,5,np.nan,1,0],
'E':[np.nan,3,6,np.nan,2,4],
'F':list('aaabbb')
})
means = pd.Series([10,20], index=['B','E'])
df= df.fillna(means)
print (df)
A B C D E F
0 a 4.0 7 1.0 20.0 a
1 b 10.0 8 3.0 3.0 a
2 c 4.0 9 5.0 6.0 a
3 d 5.0 4 NaN 20.0 b
4 e 5.0 2 1.0 2.0 b
5 f 4.0 3 0.0 4.0 b
If need replace missing values in all numeric columns use DataFrame.fillna by mean - it working because mean exclude non numeric columns:
df = pd.DataFrame({
'A':list('abcdef'),
'B':[4,np.nan,4,5,5,4],
'C':[7,8,9,4,2,3],
'D':[1,3,5,np.nan,1,0],
'E':[np.nan,3,6,np.nan,2,4],
'F':list('aaabbb')
})
df1 = df.fillna(df.mean())
print (df1)
A B C D E F
0 a 4.0 7 1.0 3.75 a
1 b 4.4 8 3.0 3.00 a
2 c 4.0 9 5.0 6.00 a
3 d 5.0 4 2.0 3.75 b
4 e 5.0 2 1.0 2.00 b
5 f 4.0 3 0.0 4.00 b
If need specify columns for means only change solution with list of columns names:
cols = ['D','B']
df[cols] = df[cols].fillna(df[cols].mean())
print (df)
A B C D E F
0 a 4.0 7 1.0 NaN a
1 b 4.4 8 3.0 3.0 a
2 c 4.0 9 5.0 6.0 a
3 d 5.0 4 2.0 NaN b
4 e 5.0 2 1.0 2.0 b
5 f 4.0 3 0.0 4.0 b
try this, for those column which you want to fill.
df['column1'] = df['column1'].fillna((df['column1'].mean()))
I have a following dataframe:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4
Now I want to fill null values of A with the values in B or D. i.e. if the value is Null in B than check D. So resultant dataframe looks like this.
A B C D
0 2.0 2.0 NaN 0
1 3.0 4.0 NaN 1
2 5 NaN NaN 5
3 3.0 3.0 NaN 4
I can do this using following code:
df['A'] = df['A'].fillna(df['B'])
df['A'] = df['A'].fillna(df['D'])
But I want to do this in one line, how can I do that?
You could simply chain both .fillna():
df['A'] = df.A.fillna(df.B).fillna(df.D)
A B C D
0 2.0 2.0 NaN 0
1 3.0 4.0 NaN 1
2 5.0 NaN NaN 5
3 3.0 3.0 NaN 4
Or using fillna with combine_first:
df['A'] = df.A.fillna(df.B.combine_first(df.D))
If dont need chain because many columns better is use back filling missing values with seelcting first column by positions:
df['A'] = df['A'].fillna(df[['B','D']].bfill(axis=1).iloc[:, 0])
print (df)
A B C D
0 2.0 2.0 NaN 0
1 3.0 4.0 NaN 1
2 5.0 NaN NaN 5
3 3.0 3.0 NaN 4
I am trying to create a very large dataframe, made up of one column from many smaller dataframes (renamed to the dataframe name). I am using CONCAT() and looping through dictionary values which represent dataframes, and looping over index values, to create the large dataframe. The CONCAT() join_axes is the common index to all the dataframes. This works fine, however I then have duplicate column names.
I must be able to loop over the indexes at specifc windows as part of my final dataframe creation - so removing this step isnt an option
For example, this results in the following final dataframe with duplciate columns:
Is there any way I can use CONCAT() excatly as I am, but merge the columns to produce an output like so?:
I think you need:
df = pd.concat([df1, df2])
Or if have duplicates in columns use groupby where if some values are overlapping then are summed:
print (df.groupby(level=0, axis=1).sum())
Sample:
df1 = pd.DataFrame({'A':[5,8,7, np.nan],
'B':[1,np.nan,np.nan,9],
'C':[7,3,np.nan,0]})
df2 = pd.DataFrame({'A':[np.nan,np.nan,np.nan,2],
'B':[1,2,np.nan,np.nan],
'C':[np.nan,6,np.nan,3]})
print (df1)
A B C
0 5.0 1.0 7.0
1 8.0 NaN 3.0
2 7.0 NaN NaN
3 NaN 9.0 0.0
print (df2)
A B C
0 NaN 1.0 NaN
1 NaN 2.0 6.0
2 NaN NaN NaN
3 2.0 NaN 3.0
df = pd.concat([df1, df2],axis=1)
print (df)
A B C A B C
0 5.0 1.0 7.0 NaN 1.0 NaN
1 8.0 NaN 3.0 NaN 2.0 6.0
2 7.0 NaN NaN NaN NaN NaN
3 NaN 9.0 0.0 2.0 NaN 3.0
print (df.groupby(level=0, axis=1).sum())
A B C
0 5.0 2.0 7.0
1 8.0 2.0 9.0
2 7.0 NaN NaN
3 2.0 9.0 3.0
What you want is df1.combine_first(df2). Refer to pandas documentation.