Histogram

df.plot(kind='hist', y=data_col, bins=the_bins, density=True, ec='w')

Create a histogram using a DataFrame.

Input:

kind : string

To create a histogram, use kind='hist'

y : label

Column to calculate frequency/probability density.

bins : integer or array, default None

If integer, number of bins to use.

If array, specifies the bin edges.

If not specified, matplotlib tries to calculate the optimal number of bins.

density : boolean, default False

If False, y-axis plots the number of observations.

If True, y-axis plots the probability density.

ec : string

Color for histogram bin outlines, use ec='w' for a white outline.

Returns:

plot - Matplotlib plot created using parameters.

Return Type:

Matplotlib plot

Note:

• In a histogram, only the last bin is inclusive of the right endpoint! [left, right)
• The bins you specity don't have to include all data values; data values not in any bin won't be shown in the histogram.

---

pets.plot(kind='hist', y='Age', bins=np.arange(0, 15, 3), density=True, ec='w')

pets.plot(kind='hist', y='Weight', bins=5, density=True, ec='w')

# DataFrame with weights of only cats and dogs.
cats_dogs = bpd.DataFrame().assign(
    cats=np.array(pets[pets.get('Species') == 'cat'].get('Weight')),
    dogs=np.array(pets[pets.get('Species') == 'dog'].get('Weight')),
)

# Overlaid histogram.
bins = np.arange(0, 40, 15)
cats_dogs.plot(kind='hist', alpha=0.65, density=True, ec='w', bins=bins)

# Without the `bins` parameter.
pets.plot(kind='hist', y='Weight', density=True, ec='w')