Pandas reindex()

import pandas as pd

# create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

# use reindex() to change the row
reindexed_df = df.reindex([0, 1, 2, 3])

print(reindexed_df)

'''
Output

     A    B
0  1.0  4.0
1  2.0  5.0
2  3.0  6.0
3  NaN  NaN
'''

reindex() Syntax

The syntax of the reindex() method in Pandas is:

obj.reindex(labels=None, index=None, columns=None, method=None, fill_value=None, limit=None, tolerance=None, copy=True)

reindex() Arguments

The reindex() method takes the following arguments:

• labels (optional) - new sequence of labels
• index (optional) - new sequence for the index (row labels)
• columns (optional) - new sequence for the column labels
• method (optional) - specifies the method to use for filling holes in the reindexed data
• fill_value (optional) - substitute value to use when introducing missing data
• limit (optional) - specifies the maximum number of consecutive elements to forward or backward fill
• tolerance (optional) - specifies the maximum distance between the index and indexer values
• copy (optional) - specifies whether to always copy the data, default is True.

reindex() Return Value

The reindex() method returns an object (Series or DataFrame) conformed to the new index and columns.

Example 1: reindex() with columns Argument

import pandas as pd

# create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}
df = pd.DataFrame(data)

# use reindex() to change the columns
reindexed_df = df.reindex(columns=['B', 'A', 'C'])

print(reindexed_df)

Output

   B  A   C
0  4  1 NaN
1  5  2 NaN
2  6  3 NaN

In this example, we rearranged the columns and added a new column C to the DataFrame using reindex().

Here,

• The order of existing columns A and B was changed.
• A new column C with default NaN values was added.
• The original columns of df, [A, B], were changed in reindexed_df to [B, A, C].

Example 2: Using fill_value with reindex()

import pandas as pd

# sample DataFrame
data = {'Values': [10, 20, 30]}
df = pd.DataFrame(data, index=['a', 'b', 'c'])

# reindex and fill missing values with 0
reindexed_df = df.reindex(['a', 'x', 'b', 'y', 'c'], fill_value=0)

print(reindexed_df)

Output

   Values
a     10
x      0
b     20
y      0
c     30

In the example above, we filled the missing values with 0 instead of NaN using the fill_value argument.

Example 3: reindex() with method Argument

import pandas as pd

# sample DataFrame with numeric index
data = {'Value': [10, 20, 30]}
df = pd.DataFrame(data, index=[0, 2, 4])

# use reindex and forward fill values
reindexed_df = df.reindex([0, 1, 2, 3, 4], method='ffill')

print(reindexed_df)

Output

   Value
0     10
1     10
2     20
3     20
4     30

Here, we passed ffill to the method argument to fill the missing values. The ffill method performs forward fill, meaning that the value at the previous index is filled in case of a missing value.

Example 4: Using the method and limit Arguments

import pandas as pd

# sample DataFrame with numeric index
data = {'Value': [10, 20, 30]}
df = pd.DataFrame(data, index=[0, 5, 10])

# use reindex with backward fill up to 2 places
reindexed_df = df.reindex(range(12), method='bfill', limit=2)

print(reindexed_df)

Output

    Value
0    10.0
1     NaN
2     NaN
3    20.0
4    20.0
5    20.0
6     NaN
7     NaN
8    30.0
9    30.0
10   30.0
11    NaN

In this example, we used the method argument with bfill for backward filling and used the limit argument to restrict the filling to 2 places.

All other missing values except the first two values where backward filling is possible are NaN values.

Example 5: Using tolerance with Float Index

import pandas as pd

# sample DataFrame with float index
data = {'Value': [10, 20, 30]}
df = pd.DataFrame(data, index=[0.0, 1.4, 3.0])

# reindex with tolerance
reindexed_df = df.reindex([0, 1, 2, 3], method='nearest', tolerance=0.5)

print(reindexed_df)

Output

   Value
0   10.0
1   20.0
2    NaN
3   30.0

Here, the tolerance argument defines the maximum distance between the desired and existing index values for filling.

Since the distance between 2 and 1.4 is 0.6 (>0.5), the nearest fill method is not applied in index 2.