# How to Change Number of Bins Used in Pandas Histogram

You can use the bins argument to modify the number of bins used in a pandas histogram:

```df.plot.hist(columns=['my_column'], bins=10)
```

The default number of bins used in a pandas histogram is 10.

The following example shows how to use the bins argument in practice.

## Example: How to Change Number of Bins in Pandas Histogram

Suppose we have the following pandas DataFrame that contains information about points scored by basketball players on various teams:

```import pandas as pd
import numpy as np

#make this example reproducible
np.random.seed(1)

#create DataFrame
df = pd.DataFrame({'team': np.repeat(['A', 'B', 'C'], 100),
'points': np.random.normal(loc=20, scale=2, size=300)})

team     points
0    A  23.248691
1    A  18.776487
2    A  18.943656
3    A  17.854063
4    A  21.730815
```

If we create a histogram to visualize the distribution of values for the points variable, pandas will use 10 bins in the histogram by default:

```#create histogram to visualize distribution of points
df.plot.hist(column=['points'], edgecolor='black')
```

Notice how there are 10 bars in the histogram.

However, suppose we use the bins argument to change the total number of bins to 20:

```#create histogram with 20 bins
df.plot.hist(column=['points'], edgecolor='black', bins=20)```

Notice how there are now 20 bars in the histogram.

We could also decrease the number of bins to 5:

```#create histogram with 5 bins
df.plot.hist(column=['points'], edgecolor='black', bins=5)```

There are now 5 total bars in the histogram.

Feel free to adjust the value for the bins argument to create the exact histogram you’d like.

However, keep the following in mind when choosing the number of bins:

• If you choose too few bins, the true underlying pattern in the data may be hidden.
• If you choose too many bins, you may just be visualizing the noise in the data.

One helpful way to determine the optimal number of bins to use in a histogram is to use Sturges’ Rule.