How to plot multiple dataframes in subplots

You can see e.gs. in the documentation demonstrating joris answer. Also from the documentation, you could also set subplots=True and layout=(,) within the pandas plot function:

df.plot(subplots=True, layout=(1,2)) 

You could also use fig.add_subplot() which takes subplot grid parameters such as 221, 222, 223, 224, etc. as described in the post here. Nice examples of plot on pandas data frame, including subplots, can be seen in this ipython notebook.

You can plot multiple subplots of multiple pandas data frames using matplotlib with a simple trick of making a list of all data frame. Then using the for loop for plotting subplots.

Working code:

import matplotlib.pyplot as plt import pandas as pd import numpy as np # dataframe sample data df1 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) df2 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) df3 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) df4 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) df5 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) df6 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B']) #define number of rows and columns for subplots nrow=3 ncol=2 # make a list of all dataframes df_list = [df1 ,df2, df3, df4, df5, df6] fig, axes = plt.subplots(nrow, ncol) # plot counter count=0 for r in range(nrow): for c in range(ncol): df_list[count].plot(ax=axes[r,c]) count+=1 

Using this code you can plot subplots in any configuration. You need to define the number of rows nrow and the number of columns ncol. Also, you need to make list of data frames df_list which you wanted to plot.

You can use the familiar Matplotlib style calling a figure and subplot, but you simply need to specify the current axis using plt.gca(). An example:

plt.figure(1) plt.subplot(2,2,1) df.A.plot() #no need to specify for first axis plt.subplot(2,2,2) df.B.plot(ax=plt.gca()) plt.subplot(2,2,3) df.C.plot(ax=plt.gca()) 

etc...

You can use this:

fig = plt.figure() ax = fig.add_subplot(221) plt.plot(x,y) ax = fig.add_subplot(222) plt.plot(x,z) ... plt.show() 

You may not need to use Pandas at all. Here's a matplotlib plot of cat frequencies:

x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2) f, axes = plt.subplots(2, 1) for c, i in enumerate(axes): axes[c].plot(x, y) axes[c].set_title('cats') plt.tight_layout() 

import numpy as np import pandas as pd imoprt matplotlib.pyplot as plt fig, ax = plt.subplots(2,2) df = pd.DataFrame({'A':np.random.randint(1,100,10), 'B': np.random.randint(100,1000,10), 'C':np.random.randint(100,200,10)}) for ax in ax.flatten(): df.plot(ax =ax) 

Option 1: Create subplots from a dictionary of dataframes with long (tidy) data

• Assumptions:
  • There is a dictionary of multiple dataframes of tidy data that are either:
    • Created by reading in from files
    • Created by separating a single dataframe into multiple dataframes
  • The categories, cat, may be overlapping, but all dataframes don't necessarily contain all values of cat
  • hue='cat'
• This example uses a dict of dataframes, but a list of dataframes would be similar.
• If the dataframes are wide, use pandas.DataFrame.melt to convert them to long form.
• Because dataframes are being iterated through, there's no guarantee that colors will be mapped the same for each plot
  • A custom color map needs to be created from the unique 'cat' values for all the dataframes
  • Since the colors will be the same, place one legend to the side of the plots, instead of a legend in every plot
• Tested in python 3.10, pandas 1.4.3, matplotlib 3.5.1, seaborn 0.11.2

Imports and Test Data

import pandas as pd import numpy as np # used for random data import matplotlib.pyplot as plt from matplotlib.patches import Patch # for custom legend - square patches from matplotlib.lines import Line2D # for custom legend - round markers import seaborn as sns import math import ceil # determine correct number of subplot # synthetic data df_dict = dict() for i in range(1, 7): np.random.seed(i) # for repeatable sample data data_length = 100 data = {'cat': np.random.choice(['A', 'B', 'C'], size=data_length), 'x': np.random.rand(data_length), 'y': np.random.rand(data_length)} df_dict[i] = pd.DataFrame(data) # display(df_dict[1].head()) cat x y 0 B 0.944595 0.606329 1 A 0.586555 0.568851 2 A 0.903402 0.317362 3 B 0.137475 0.988616 4 B 0.139276 0.579745 # display(df_dict[6].tail()) cat x y 95 B 0.881222 0.263168 96 A 0.193668 0.636758 97 A 0.824001 0.638832 98 C 0.323998 0.505060 99 C 0.693124 0.737582 

Create color mappings and plot

# create color mapping based on all unique values of cat unique_cat = {cat for v in df_dict.values() for cat in v.cat.unique()} # get unique cats colors = sns.color_palette('tab10', n_colors=len(unique_cat)) # get a number of colors cmap = dict(zip(unique_cat, colors)) # zip values to colors col_nums = 3 # how many plots per row row_nums = math.ceil(len(df_dict) / col_nums) # how many rows of plots # create the figure and axes fig, axes = plt.subplots(row_nums, col_nums, figsize=(9, 6), sharex=True, sharey=True, tight_layout=True) # convert to 1D array for easy iteration axes = axes.flat # iterate through dictionary and plot for ax, (k, v) in zip(axes, df_dict.items()): sns.scatterplot(data=v, x='x', y='y', hue='cat', palette=cmap, ax=ax) sns.despine(top=True, right=True) ax.legend_.remove() # remove the individual plot legends ax.set_title(f'dataset = {k}', fontsize=11) # create legend from cmap # patches = [Patch(color=v, label=k) for k, v in cmap.items()] # square patches patches = [Line2D([0], [0], marker='o', color='w', markerfacecolor=v, label=k, markersize=8) for k, v in cmap.items()] # round markers # place legend outside of plot; change the right bbox value to move the legend up or down plt.legend(title='cat', handles=patches, bbox_to_anchor=(1.06, 1.2), loc='center left', borderaxespad=0, frameon=False) plt.show() 

Option 2: Create subplots from a single dataframe with multiple separate datasets

• The dataframes must be in a long form with the same column names.
• This option uses pd.concat to combine multiple dataframes into a single dataframe, and .assign to add a new column.
  • See Import multiple csv files into pandas and concatenate into one DataFrame for creating a single dataframes from a list of files.
• This option is easier because it doesn't require manually mapping colors to 'cat'

Combine DataFrames

# using df_dict, with dataframes as values, from the top # combine all the dataframes in df_dict to a single dataframe with an identifier column df = pd.concat((v.assign(dataset=k) for k, v in df_dict.items()), ignore_index=True) # display(df.head()) cat x y dataset 0 B 0.944595 0.606329 1 1 A 0.586555 0.568851 1 2 A 0.903402 0.317362 1 3 B 0.137475 0.988616 1 4 B 0.139276 0.579745 1 # display(df.tail()) cat x y dataset 595 B 0.881222 0.263168 6 596 A 0.193668 0.636758 6 597 A 0.824001 0.638832 6 598 C 0.323998 0.505060 6 599 C 0.693124 0.737582 6 

Plot a FacetGrid with seaborn.relplot

g = sns.relplot(kind='scatter', data=df, x='x', y='y', hue='cat', col='dataset', col_wrap=3, height=3) 

• Both options create the same result, however, it's less complicated to combine all the dataframes, and plot a figure-level plot with sns.relplot.

Building on @joris response above, if you have already established a reference to the subplot, you can use the reference as well. For example,

ax1 = plt.subplot2grid((50,100), (0, 0), colspan=20, rowspan=10) ... df.plot.barh(ax=ax1, stacked=True) 

Here is a working pandas subplot example, where modes is the column names of the dataframe.

 dpi=200 figure_size=(20, 10) fig, ax = plt.subplots(len(modes), 1, sharex="all", sharey="all", dpi=dpi) for i in range(len(modes)): ax[i] = pivot_df.loc[:, modes[i]].plot.bar(figsize=(figure_size[0], figure_size[1]*len(modes)), ax=ax[i], title=modes[i], color=my_colors[i]) ax[i].legend() fig.suptitle(name)