. Advertisement .

..3..

. Advertisement .

..4..

Pandas should never be a strange name for coders, which is a powerful toolbox library. It contains two main data structures: DataFrame and Series and a crucial parameter: axis.

While it is a key factor for the efficient library operation, it is often poorly described and underrated by various coding beginners. This article will offer a deeper understanding of Pandas axis meaning and its uses.

**What Is Axis In Pandas?**

Axis or axes are identified for an array having more than one dimension. For example, with a dual-dimensional array, you will receive two axes: axis 0 and axis 1.

Specifically, the axis 0 runs downwards vertically across the rows. On the other hand, axis 1 runs across columns horizontally. Each axis shows different values.

**Axis = 0 **

The axis=0 represents the column-wise function. So when the axis returns to zero, it means that your program is performing effectively and satisfies the condition.

Here is an example of this case:

Code:

```
# importing packages
import pandas as pd
# importing our dataset
df = pd.read_csv('hiring.csv')
# dropping the column named 'experience'
df = df.drop([0, 3], axis=0)
# 'viewing the dataframe
df.head()
```

**Axis = 1**

On the other hand, the axis=1 parameter represents the row-wise DataFrame function. When you get the axis set to this value, it means that the program is satisfying the column condition.

```
# importing packages
import pandas as pd
# importing our dataset
df = pd.read_csv('hiring.csv')
# dropping the column named 'experience'
df = df.drop(['experience'], axis=1)
# 'viewing the dataframe
df.head()
```

**Some Uses Of Axis**

**Find The Mean Value**

One of the very first uses of axis is to determine the mean value for both columns and rows. Use the axis=0 command to get the column’s mean value:

```
#find mean of each column
df.mean(axis=0)
points 20.250
assists 7.750
rebounds 8.375
dtype: float64
```

The output shows the numeric column value. In this case, it is worth noting that the program doesn’t calculate all the character columns automatically.

The axis=1 command is used to find the row’s mean value:

```
#find mean of each row
df.mean(axis=1)
0 13.666667
1 9.000000
2 10.666667
3 9.666667
4 12.333333
5 12.333333
6 14.333333
7 15.000000
dtype: float64
```

From the output, you can easily get the mean value of each row.

**Find The Sum Value**

Like finding the mean value, each axis will work out the result for each corresponding line. In particular, the axis=0 gives the sum of some chosen DataFrame columns while the axis=1 finds that of chosen rows.

```
#find sum of each row
df.sum(axis=1)
0 41
1 27
2 32
3 29
4 37
5 37
6 43
7 45
dtype: int64
```

**Find The Max Value**

Find the max value in specific columns with the following code:

```
#find max of 'points', 'assists', and 'rebounds' columns
df[['points', 'assists', 'rebounds']].max(axis=0)
points 29
assists 12
rebounds 12
dtype: int64
```

For rows, use the general command:

```
#find max of each row
df.max(axis=1)
```

**Conclusion**

The article discusses what Pandas axis is and its applications in calculating the values. Sometimes, you only have to work on some rows or some columns. That’s where the axis parameter comes in handy.

Check the post above to see when to use axis=0 and axis=1.

## Leave a comment