Reset the Index of a pandas DataFrame

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reset_index¶

By default reset_index returns a copy rather than modify the original data frame. You can specify inplace = True to overwrite the behavior.

Series¶

If you drop the original index, you still have a Series. However, if you reset index of a sereis without dropping the original index, you get a data frame.

s = pd.Series([1, 2, 3, 4], index=["r1", "r2", "r3", "r4"])
s

r1    1
r2    2
r3    3
r4    4
dtype: int64

df = s.reset_index()
df

df = s.reset_index(drop=True)
df

0    1
1    2
2    3
3    4
dtype: int64

DataFrame¶

import pandas as pd

df = pd.DataFrame(
    {"x": [1, 2, 3, 4, 5], "y": [5, 4, 3, 2, 1]}, index=["r1", "r2", "r3", "r4", "r5"]
)

df.head()

# keep the original index as a new column and create a new index
df.reset_index()

# drop the original index and create a new index
df.reset_index(drop=True)

Multi-index¶

import pandas as pd

df = pd.DataFrame(
    {"x": [1, 2, 3, 4, 5], "y": [5, 4, 3, 2, 1]},
    index=pd.MultiIndex.from_tuples(
        [("r1", 0), ("r2", 1), ("r3", 2), ("r4", 3), ("r5", 4)]
    ),
)

df.head()

df.reset_index()

df.reset_index(drop=True)

# drops the 2nd index and keep the first index
df.reset_index(level=1, drop=True)