**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.

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.