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# Adding a prefix to column values in Pandas

schedule Aug 12, 2023
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PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":["a","b"],"B":[4,5]})df A B0 a 41 b 5 ```

# Adding prefix to a single column

To add a prefix to each value in column `A`:

``` df["A"] = "c" + df["A"]df A B0 ca 41 cb 5 ```

For non-string typed columns, you must first convert its type to string using `astype(str)`:

``` df["B"] = "d" + df["B"].astype(str)df A B0 a d41 b d5 ```

# Adding prefix to multiple columns

To add a prefix to multiple columns:

``` df[["A","B"]] = "c" + df[["A","B"]].astype(str)df A B0 ca c41 cb c5 ```

Here, `df[["A","B"]]` returns a DataFrame, and we convert the type of all its columns to `string` using `astype(str)`.

Consider the following DataFrame:

``` df = pd.DataFrame({"A":["aa","b"],"B":[4,55]})df A B0 aa 41 b 55 ```

## Single column

To add a padding to each value in a column until the desired width is reached, use Series' `str.pad(~)` method:

``` df["A"] = df["A"].str.pad(width=3, fillchar="#") # side="left"df A B0 #aa 41 ##b 55 ```

Note that `str.pad(~)` throws an error if all the column values are not of `string` type. To pad non-string columns, use `astype(str)` to convert them to `string` type:

``` df["B"] = df["B"].astype(str).str.pad(width=3, fillchar="#")df A B0 aa ##41 b #55 ```

## Multiple columns

Consider the following DataFrame:

``` df = pd.DataFrame({"A":["aa","b"],"B":["5","66"]})df A B0 aa 51 b 66 ```

The method `str.pad(~)` is only available for `Series`, and so we cannot pad multiple columns at the same time by simply extracting them as a DataFrame (`df[["A","B"]]`).

Instead, we can loop through a list of column labels like so:

``` columns_to_pad = ["A","B"]for col_label in columns_to_pad: df[col_label] = df[col_label].str.pad(width=3, fillchar="#")df A B0 #aa ##51 ##b #66 ```
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