 # Find maximum values & position in columns and rows of a Dataframe in Pandas

In this article, we are going to discuss how to find maximum value and its index position in columns and rows of a Dataframe.

DataFrame.max()

Pandas dataframe.max() method finds the maximum of the values in the object and returns it. If the input is a series, the method will return a scalar which will be the maximum of the values in the series. If the input is a dataframe, then the method will return a series with maximum of values over the specified axis in the dataframe. The index axis is the default axis taken by this method.

Syntax : DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns : max : Series or DataFrame (if level specified)

Let’s take an example of how to use this function. Let’s suppose we have a Dataframe.

## How to Find Maximum Values of Every Column?

To find the maximum value of each column in a Pandas dataframe, we can use the `max()` method. This method will return the maximum value of each column in the dataframe.

```		import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
max_values = df.max()

print(max_values)
```

Output:

``````
A    3
B    6
C    9
dtype: int64
``````

## How to Find Maximum Values of Every Row?

To find the maximum value of each row in a Pandas dataframe, we can use the `max()` method along the axis 1. This method will return the maximum value of each row in the dataframe.

```	import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
max_values = df.max(axis=1)

print(max_values)
```

Output:

``````
0    7
1    8
2    9
dtype: int64
``````

## How to Find Maximum Values of Every Column Without Skipping NaN?

To find the maximum value of each column in a Pandas dataframe, including NaN values, we can use the `max()` method with the `skipna` parameter set to `False`. This method will return the maximum value of each column in the dataframe, including NaN values.

```	import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, np.nan],
'B': [4, np.nan, 6], 'C': [7, 8, 9]})

max_values = df.max(skipna=False)

print(max_values)
```

Output:

``````
A    NaN
B    NaN
C    9.0
dtype: float64
``````

Another useful method in pandas to find the maximum values of every row is the `max()` method with the `axis=1` parameter. This method will find the maximum value of each row in the DataFrame.

## How to find maximum values of every row?

```import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# find maximum values of every row

max_values = df.max(axis=1)

print(max_values)
```

Output:
``` 0 7 1 8 2 9 dtype: int64 ```

Now, what if there are NaN values in the DataFrame? By default, the `max()` method skips NaN values. However, we can include them in the calculation by setting the `skipna` parameter to `False`. Here’s an example:

## How to find maximum values of every column without skipping NaN?

```import pandas as pd
import numpy as np
# create a sample DataFrame with NaN values

df = pd.DataFrame({'A': [1, 2, np.nan],
'B': [4, np.nan, 6], 'C': [7, 8, 9]})

# find maximum values of every column without skipping NaN
max_values = df.max(skipna=False)

print(max_values)
```

Output:
``` A NaN B NaN C 9.0 dtype: float64 ```

Now, what if we want to find the maximum values of a single column or selected columns only? We can pass the column name or a list of column names to the `max()` method. Here’s an example:

## How to find maximum values of a single column or selected columns?

```import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# find maximum values of selected columns

max_values = df[['A', 'C']].max()

print(max_values)
```

Output:
``` A 3 C 9 dtype: int64 ```

In addition to finding the maximum values, we can also get their positions in the DataFrame using the `idxmax()` method. This method returns the index label of the first occurrence of the maximum value in each column or row. Here are some examples:

## How to get position of maximum values of every column?

```import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# get position of maximum values of every column

max_positions = df.idxmax()

print(max_positions)
```

Output:
``` A 2 B 2 C 2 dtype: int64 ```

Last Updated on May 16, 2023 by admin

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