**Pandas Max()**: We will see in this tutorial how to use the max() function for a column in a Pandas dataframe.

**Introduction**

A** pandas dataframe** is a two-dimensional tabular data structure that can be modified in size with labeled axes that are commonly referred to as **row **and **column **labels, with different **arithmetic operations **aligned with the row and column labels.

The Pandas library, available on python, allows to import data and to make quick analysis on loaded data.

Max() is also included within **Pandas Describe**.

In this tutorial, we will see how to use the** max() function** of the **Pandas **library. This function allows to find the maximum value of a column DataFrame. We will see in this tutorial how to get :

- The maximum value of a numeric column
- The maximum value of a string column
- The index of the maximum value of the column

To illustrate these different points, we will use this Pandas Dataframe:

```
import pandas as pd
powers = {'Heros': ['Thor', 'Spiderman', 'Iron man', 'Captain America', 'Hulk'],
'Power': [50, 40, 55, 45, 80],
'First_appearance': [1962, 1962, 1963, 1941, 1962]
}
df = pd.DataFrame(powers, columns=['Heros', 'Power', 'First_appearance'])
print(df)
```

This dataframe contains 3 columns: the name of the hero, his power and the date of first appearance in the Marvel comics

## Pandas Dataframe Max() function

### Pandas Max() Syntax

The max() function provided in the Pandas library returns the maximum of the values for the requested axis.

Its syntax is as follows:

```
DataFrame.max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
```

The function takes into consideration 5 parameters:

Name | Description | Type | Default Value | Required |

axis | The axis to apply the function ( 0=index,1=columns) | {index (0), columns (1)} | – | Yes |

skipna | Exclude NA / NULL values | Boolean | True | Yes |

level | If the axis is a MultiIndex (hierarchical), count along a particular level, reducing to a series. | int or level name | None | Yes |

numeric_only | Include only float, int, boolean columns. If none, will try to use everything, then use only numeric data. Not implemented for the series. | Boolean | None | Yes |

**kwargs | Additional arguments to be passed to the function. | – | – | Yes |

The function returns a Series or a Dataframe if the level is specified.

## Pandas dataframe.max()

We will start from our Pandas dataframe :

```
import pandas as pd
powers = {'Heros': ['Thor', 'Spiderman', 'Iron man', 'Captain America', 'Hulk'],
'Power': [50, 40, 55, 45, 80],
'First_appearance': [1962, 1962, 1963, 1941, 1962]
}
df = pd.DataFrame(powers, columns=['Heros', 'Power', 'First_appearance'])
print(df)
```

```
Heros Power First_appearance
0 Thor 50 1962
1 Spiderman 40 1962
2 Iron man 55 1963
3 Captain America 45 1941
4 Hulk 80 1962
```

To calculate the maximum value of each column, we will use the max function with the parameter **axis=0**

```
# Max Value
print(df['Heros'].max(axis=0))
print(df['Power'].max(axis=0))
print(df['First_appearance'].max(axis=0))
```

```
Thor
80
1963
```

The max() function works on both **numeric **and **string **columns.

## Pandas dataframe.idxmax()

The max() function allows to get the maximum value of a dataframe column. There is a function called idxmax() which allows to get the index of the maximum value of the column.

```
# The index of the maximum value
print(df['Power'].idxmax())
print(df['First_appearance'].idxmax())
```

```
4
2
```

## Conclusion

We have seen in this tutorial that it is quite simple to do arithmetic operations on columns of a pandas dataframe. The max() function is a very used function in data analysis to have a simple description of the data.

If you have any questions about its use, don’t hesitate to leave me a comment, I will be happy to answer them.

See you soon for new tutorials.