reindex() function is used to change the index of rows and columns of a DataFrame. It changes the row label and column label to a dataframe.
Reindexing is used to conform DataFrame to new index with optional filling logic and to place NaN in that location where the values are not present in the previous index. It produce a new object unless the new indexis equivalent to the current one, and values of copy becomes False.
We can reindex the single or multiple rows by using the reindex() method.
Syntax
DataFrame.reindex(labels=None, index=None, columns=None, axis=None, method=None, copy=True, level=None, fill_value=nan, limit=None, tolerance=None)Parameters:
- labels: It refers to the new labels or the index to conform to the axis that is specified by the 'axis'.
- index , columns : It is optional. It is prefers an index object for avoiding the duplicate data.
- axis: It is used to give number or axis name which is to be targeted.
- method: It is used to fill the holes in reindexed DataFrame.
- copy: It returns a new object, even if the passed indexes are the same.
- level: It broadcast across a level, matching Index values on the passed MultiIndex level.
- fill_value: It is the values which are used in place of missing values.
- limit: The maximum number of consecutive elements that are to be forward or backward fill.
- tolerance:The maximum distance between original and new labels for inexact matches.
Return:
It return series or dataframe with changed index.
Example
import pandas as pd
df=pd.DataFrame({'Name':['Alice','John','Joey'],'Age':[19,15,14],
'Height':[145.7,152.6,148.7]})
index = [1,2,3]
df.index=index
print(df)Output
Name Age Height
1 Alice 19 145.7
2 John 15 152.6
3 Joey 14 148.7
new_index=[20,2,3]
print(df.reindex(new_index))Output
Name Age Height
20 NaN NaN NaN
2 John 15.0 152.6
3 Joey 14.0 148.7We can fill the missing values by using fill_value
df.reindex(new_index, fill_value=0)Output
Name Age Height
20 0 0 0
2 John 15.0 152.6
3 Joey 14.0 148.7