How to Plot Multiple Bar Plots in Pandas and Matplotlib

To plot multiple bar or line plots in Pandas and Matplotlib we can use plt.subplots()

Steps

Import libraries
Load and prepare data
Select number of rows/columns for subplots
- row_num = 3; col_num = 4
Create subplots
- select chart size
- set subplot position by - fig, axes = plt.subplots(row_num, col_num)
Plot subplots
- select chart type
  - bar
  - line chart
  - scatter plot
- Add title
Show plot

More information can be found: Creating multiple subplots using

Data

We are loading the seaborn dataset for flights. Additionally we are mapping the month to season in order to get categorical data.

year	1949	1950	1951	1952	1953	1954	1955	1956	1957	1958	1959	1960
month
Jan	112	115	145	171	196	204	242	284	315	340	360	417
Feb	118	126	150	180	196	188	233	277	301	318	342	391
Mar	132	141	178	193	236	235	267	317	356	362	406	419
Apr	129	135	163	181	235	227	269	313	348	348	396	461
May	121	125	172	183	229	234	270	318	355	363	420	472

Example

To plot scatter plot with best fit line we use:

sns.lmplot()
provide X and T
plt.ylabel('Y')

Full example:

import matplotlib.pyplot as plt
import seaborn as sns; sns.set_theme()

flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")

df = flights

cols = df.columns
row_num = 3
col_num = 4

fig, axes = plt.subplots(row_num, col_num, squeeze=False, figsize=(20,10))

row_n = -1
for ix, col in enumerate(cols):

    col_n = ix % col_num
    if col_n == 0:
        row_n = row_n + 1
    df[col].plot(kind='bar', x=col_n, ax=axes[row_n, col_n], title=col)

plt.show()

Note: for plotting multiple plots on single row we can use:

cols = df.columns

fig, axes = plt.subplots(1, len(cols), figsize=(30,5), squeeze=False)

for i, col in enumerate(cols):
    df[col].plot(kind='bar', x=col, ax=axes[0, i], title=col)

plt.show()

Steps

Data

Example

Output