Pandas Plotting

Getting Started with df.plot()

Every DataFrame and Series has a .plot() accessor that builds a matplotlib chart directly from your data, so you don't have to hand columns off to matplotlib yourself. It is a convenience wrapper: pandas figures out the axes and data for you, but matplotlib does the actual drawing, which means matplotlib must be installed and every customization option matplotlib exposes is still reachable through the returned Axes object.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'revenue': [12000, 15500, 14200, 18900, 21000, 19800]
})

df.plot(x='month', y='revenue', kind='line', title='Monthly Revenue')
plt.show()

Choosing a Plot Kind

The kind parameter controls what type of chart gets drawn. It defaults to 'line', which works well for data ordered by an index such as dates, but pandas supports several other kinds suited to different shapes of data.

• line (default) connects points in order, ideal for trends over time.
• bar / barh draws vertical or horizontal bars for comparing categories.
• hist draws a histogram showing the distribution of a single column.
• box draws a box-and-whisker plot summarizing quartiles and outliers.
• kde draws a smoothed density curve, an alternative to a histogram.
• area draws a stacked or filled area chart for cumulative trends.
• pie shows the proportions of a whole for a single column.
• scatter plots the relationship between two numeric columns.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'product': ['Widgets', 'Gadgets', 'Gizmos', 'Doohickeys'],
    'units_sold': [340, 220, 510, 180]
})

df.plot(x='product', y='units_sold', kind='bar', color='steelblue', legend=False)
plt.ylabel('Units Sold')
plt.show()

Customizing Plots

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'advertising_spend': [200, 400, 600, 800, 1000, 1200],
    'sales': [2200, 2600, 3100, 3400, 4000, 4300]
})

ax = df.plot(
    x='advertising_spend',
    y='sales',
    kind='scatter',
    figsize=(6, 4),
    title='Advertising Spend vs. Sales',
    grid=True
)
ax.set_xlabel('Advertising Spend ($)')
ax.set_ylabel('Sales ($)')
plt.show()