Reindexing changes the row labels and column labels of a DataFrame. To reindex means to conform the data to match a given set of labels along a particular axis.
Multiple operations can be accomplished through indexing like −
- Reorder the existing data to match a new set of labels.
- Insert missing value (NA) markers in label locations where no data for the label existed.
Example
import pandas as pd
import numpy as np
N=20
df = pd.DataFrame({
'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
'x': np.linspace(0,stop=N-1,num=N),
'y': np.random.rand(N),
'C': np.random.choice(['Low','Medium','High'],N).tolist(),
'D': np.random.normal(100, 10, size=(N)).tolist()
})
#reindex the DataFrame
df_reindexed = df.reindex(index=[0,2,5], columns=['A', 'C', 'B'])
print df_reindexed
Its output is as follows −
A C B 0 2016-01-01 Low NaN 2 2016-01-03 High NaN 5 2016-01-06 Low NaN
Reindex to Align with Other Objects
You may wish to take an object and reindex its axes to be labeled the same as another object. Consider the following example to understand the same.
Example
import pandas as pd import numpy as np df1 = pd.DataFrame(np.random.randn(10,3),columns=['col1','col2','col3']) df2 = pd.DataFrame(np.random.randn(7,3),columns=['col1','col2','col3']) df1 = df1.reindex_like(df2) print df1
Its output is as follows −
col1 col2 col3 0 -2.467652 -1.211687 -0.391761 1 -0.287396 0.522350 0.562512 2 -0.255409 -0.483250 1.866258 3 -1.150467 -0.646493 -0.222462 4 0.152768 -2.056643 1.877233 5 -1.155997 1.528719 -1.343719 6 -1.015606 -1.245936 -0.295275
Note − Here, the df1 DataFrame is altered and reindexed like df2. The column names should be matched or else NAN will be added for the entire column label.
Filling while ReIndexing
reindex() takes an optional parameter method which is a filling method with values as follows −
- pad/ffill − Fill values forward
- bfill/backfill − Fill values backward
- nearest − Fill from the nearest index values
Example
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])
df2 = pd.DataFrame(np.random.randn(2,3),columns=['col1','col2','col3'])
# Padding NAN's
print df2.reindex_like(df1)
# Now Fill the NAN's with preceding Values
print ("Data Frame with Forward Fill:")
print df2.reindex_like(df1,method='ffill')
Its output is as follows −
col1 col2 col3
0 1.311620 -0.707176 0.599863
1 -0.423455 -0.700265 1.133371
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Data Frame with Forward Fill:
col1 col2 col3
0 1.311620 -0.707176 0.599863
1 -0.423455 -0.700265 1.133371
2 -0.423455 -0.700265 1.133371
3 -0.423455 -0.700265 1.133371
4 -0.423455 -0.700265 1.133371
5 -0.423455 -0.700265 1.133371
Note − The last four rows are padded.
Limits on Filling while Reindexing
The limit argument provides additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches. Let us consider the following example to understand the same −
Example
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])
df2 = pd.DataFrame(np.random.randn(2,3),columns=['col1','col2','col3'])
# Padding NAN's
print df2.reindex_like(df1)
# Now Fill the NAN's with preceding Values
print ("Data Frame with Forward Fill limiting to 1:")
print df2.reindex_like(df1,method='ffill',limit=1)
Its output is as follows −
col1 col2 col3
0 0.247784 2.128727 0.702576
1 -0.055713 -0.021732 -0.174577
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Data Frame with Forward Fill limiting to 1:
col1 col2 col3
0 0.247784 2.128727 0.702576
1 -0.055713 -0.021732 -0.174577
2 -0.055713 -0.021732 -0.174577
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
Note − Observe, only the 7th row is filled by the preceding 6th row. Then, the rows are left as they are.
Renaming
The rename() method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.
Let us consider the following example to understand this –
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])
print df1
print ("After renaming the rows and columns:")
print df1.rename(columns={'col1' : 'c1', 'col2' : 'c2'},
index = {0 : 'apple', 1 : 'banana', 2 : 'durian'})
Its output is as follows −
col1 col2 col3
0 0.486791 0.105759 1.540122
1 -0.990237 1.007885 -0.217896
2 -0.483855 -1.645027 -1.194113
3 -0.122316 0.566277 -0.366028
4 -0.231524 -0.721172 -0.112007
5 0.438810 0.000225 0.435479
After renaming the rows and columns:
c1 c2 col3
apple 0.486791 0.105759 1.540122
banana -0.990237 1.007885 -0.217896
durian -0.483855 -1.645027 -1.194113
3 -0.122316 0.566277 -0.366028
4 -0.231524 -0.721172 -0.112007
5 0.438810 0.000225 0.435479
The rename() method provides an inplace named parameter, which by default is False and copies the underlying data. Pass inplace=True to rename the data in place.