Pandas Subplots: subplots, layout and sharex

pandas can split the columns of a data frame in several subplots with a single argument. For more complex layouts create the subplots with matplotlib and pass each axes to plot.

One subplot per column

Set subplots = True to draw each column in its own subplot.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
df = pd.DataFrame({"Temperature": 25 + rng.normal(0, 1, 30).cumsum(),
                   "Humidity": 60 + rng.normal(0, 5, 30),
                   "Wind": rng.gamma(2, 4, 30),
                   "Rain": rng.exponential(2, 30)},
                  index = pd.date_range("2025-06-01", periods = 30))

df.plot(subplots = True)
# plt.show()

One subplot per column in pandas

Layout of the subplots

layout sets the number of rows and columns of the grid. Use -1 to compute one of the dimensions automatically.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
df = pd.DataFrame({"Temperature": 25 + rng.normal(0, 1, 30).cumsum(),
                   "Humidity": 60 + rng.normal(0, 5, 30),
                   "Wind": rng.gamma(2, 4, 30),
                   "Rain": rng.exponential(2, 30)},
                  index = pd.date_range("2025-06-01", periods = 30))

df.plot(subplots = True, layout = (2, 2), legend = False,
        title = list(df.columns))
# plt.show()

Grid of subplots with layout in pandas

sharex and sharey use the same limits in all the subplots, which makes it easier to compare them.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
df = pd.DataFrame({"Temperature": 25 + rng.normal(0, 1, 30).cumsum(),
                   "Humidity": 60 + rng.normal(0, 5, 30),
                   "Wind": rng.gamma(2, 4, 30),
                   "Rain": rng.exponential(2, 30)},
                  index = pd.date_range("2025-06-01", periods = 30))

df[["Wind", "Rain"]].plot(subplots = True, layout = (1, 2), sharey = True)
# plt.show()

Subplots with shared Y axis in pandas

Several columns in the same subplot

Since pandas 2.0 subplots also accepts a list of tuples to group several columns in the same subplot.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
df = pd.DataFrame({"Temperature": 25 + rng.normal(0, 1, 30).cumsum(),
                   "Humidity": 60 + rng.normal(0, 5, 30),
                   "Wind": rng.gamma(2, 4, 30),
                   "Rain": rng.exponential(2, 30)},
                  index = pd.date_range("2025-06-01", periods = 30))

df.plot(subplots = [("Wind", "Rain")], layout = (3, 1))
# plt.show()

Group several columns in the same subplot in pandas

Plot into existing axes

Create the subplots with plt.subplots and pass each axes to the ax argument. This allows combining different types of charts in the same figure.

Combine different pandas charts in matplotlib subplots

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
df = pd.DataFrame({"Temperature": 25 + rng.normal(0, 1, 30).cumsum(),
                   "Humidity": 60 + rng.normal(0, 5, 30),
                   "Wind": rng.gamma(2, 4, 30),
                   "Rain": rng.exponential(2, 30)},
                  index = pd.date_range("2025-06-01", periods = 30))

fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (8, 3.5))

df["Temperature"].plot(ax = ax1)
df[["Wind", "Rain"]].mean().plot.bar(ax = ax2, rot = 0)
# plt.show()

Learn more about how to create and customize subplots in matplotlib.