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# Getting maximum value in columns of Pandas DataFrame

schedule Aug 12, 2023
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PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4], "B":[5,6]})df A B0 3 51 4 6 ```

# Maximum value of a single column

To get the maximum value of column `A`, use the `max()` method like so:

``` df["A"].max() 4 ```

# Maximum value of each column

To get the maximum value of each column, use the `max()` method like so:

``` df[["A","B"]].max() A 4B 6dtype: int64 ```

Here, we are first extracting a DataFrame containing columns `A` and `B`.

# Maximum value of multiple columns

To get the maximum value in multiple columns:

``` df[["A","B"]].max().max() 6 ```

Here, we are first extracting the DataFrame that holds columns `A` and `B`. We then compute the max of each column:

``` df[["A","B"]].max() A 4B 6dtype: int64 ```

Since we want the maximum in both these columns, we must call `max()` again.

# Creating a new column containing the maximum of multiple columns

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4], "B":[5,6]})df A B0 3 51 4 6 ```

To add a new column `C` that holds that maximum value of each row in columns `A` and `B`:

``` df["C"] = df[["A","B"]].max(axis=1)df A B C0 3 5 51 4 6 6 ```

Here, we are first extracting columns `A` and `B` as a DataFrame. We then call `max(axis=1)` to compute the maximum of each row in this DataFrame:

``` df[["A","B"]].max(axis=1)   # returns a Series 0 51 6dtype: int64 ```
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