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# Modifying values in Index of Pandas DataFrame

schedule Aug 11, 2023
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,5],"B":[6,7,8]}, index=["a","b","a"])df A Ba 3 6b 4 7a 5 8 ```

# Replacing certain values

To replace values `a` with `c` in the index of `df`, use the `rename(~)` method:

``` df.rename(index={"a":"c"}) A Bc 3 6b 4 7c 5 8 ```

Here, a new DataFrame is returned and so the original `df` is kept intact. To modify `df` directly, set `inplace=True`.

# Completely replacing the Index

To completely replace the Index:

``` df.index = ["d","e","f"]df A Bd 3 6e 4 7f 5 8 ```

# Conditional replacement of values

Consider the same DataFrame as before:

``` df = pd.DataFrame({"A":[3,4,5],"B":[6,7,8]}, index=["a","b","a"])df A Ba 3 6b 4 7a 5 8 ```

To replace only the values in the Index that are greater than `"a"` with `"#"`:

``` s = pd.Series(df.index)s[s > "a"] = "#"df.index = sdf A Ba 3 6# 4 7a 5 8 ```

## Explanation

`Index` objects are immutable. Therefore, in order to perform conditional replacements via boolean masking, we first convert an Index into a Series:

``` s = pd.Series(df.index)s 0 a1 b2 adtype: object ```

We then get a boolean mask where `True` indicates values that are greater than `"a"`:

``` s > "a" 0 False1 True2 Falsedtype: bool ```

We then use this mask to modify only the values that correspond to `True`:

``` s[s > "a"] = "#" 0 a1 #2 adtype: object ```

Finally, we set this Series as the new Index:

``` df.index = sdf A Ba 3 6# 4 7a 5 8 ```
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