Bar Plot

df.plot(kind='bar', x=data_col, y=data_col)

Creates a bar plot using a DataFrame.

Input:

kind : string

To create a bar plot, use kind='bar'

To create a horizontal bar plot, use kind='barh'

x : label, optional

Column containing categorical data for group labels. If not specified, the index of the DataFrame is used.

y : label, optional

Column containing numerical data for the groups in x. If not specified, all numerical columns are used.

Returns:

Matplotlib plot created using parameters.

Return Type:

Matplotlib plot

Note:

When kind='bar', you can sort your DataFrame in descending order by your y column before plotting to create a vertical bar plot that goes from tall to short, left to right.
When kind='barh', you can sort your DataFrame in ascending order by your y column before plotting to create a horizontal bar plot that goes from tall to short, top to bottom.

# Creating a DataFrame to use for bar plots below
avg_species_weights = pets.get(['Species', 'Weight']).groupby('Species').mean().reset_index()
avg_species_weights

Index	Species	Weight
0	cat	8.8333333333
1	dog	48.3333333333
2	hamster	0.625

avg_species_weights.sort_values(by='Weight', ascending=False).plot(kind='bar', x='Species', y='Weight')

Bar plot example 1

If the group label is the index of the DataFrame, the x parameter can be omitted.

# Creating a DataFrame to use for bar plots below
species_count = pets.get(['Species','ID']).groupby('Species').count()
species_count = species_count.assign(Count = species_count.get('ID')).drop(columns = ['ID'])
species_count

Index	Count
cat	3
dog	3
hamster	2

species_count.plot(kind='bar', y='Count', title='Distribution of Species');

Bar plot example 2

avg_species_weights.sort_values(by='Weight', ascending=True).plot(kind='barh', x='Species', y='Weight')

Bar plot example 3

pets.groupby('Species').count().plot(kind='bar', y='ID', title='Distribution of Species')

Bar plot example 3