How can I plot separate Pandas DataFrames as subplots?

I have a few Pandas DataFrames sharing the same value scale, but having different columns and indices. When invoking df.plot(), I get separate plot images. what I really want is to have them all in the same plot as subplots, but I'm unfortunately failing to come up with a solution to how and would highly appreciate some help.

10 Answers

You can manually create the subplots with matplotlib, and then plot the dataframes on a specific subplot using the ax keyword. For example for 4 subplots (2x2):

import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) df1.plot(ax=axes[0,0]) df2.plot(ax=axes[0,1]) ... 

Here axes is an array which holds the different subplot axes, and you can access one just by indexing axes.
If you want a shared x-axis, then you can provide sharex=True to plt.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 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 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 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() 

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) 

Option 1: How to 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'
• 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

Imports and synthetic data

import pandas as pd import numpy as np # used for random data import random # used for random data import matplotlib.pyplot as plt from matplotlib.patches import Patch # for custom legend 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) random.seed(i) data_length = 100 data = {'cat': [random.choice(['A', 'B', 'C']) for _ in range(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 A 0.417022 0.326645 1 C 0.720324 0.527058 2 A 0.000114 0.885942 3 B 0.302333 0.357270 4 A 0.146756 0.908535 

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('husl', n_colors=len(unique_cat)) # get a number of colors cmap = dict(zip(unique_cat, colors)) # zip values to colors # iterate through dictionary and plot col_nums = 3 # how many plots per row row_nums = math.ceil(len(df_dict) / col_nums) # how many rows of plots plt.figure(figsize=(10, 5)) # change the figure size as needed for i, (k, v) in enumerate(df_dict.items(), 1): plt.subplot(row_nums, col_nums, i) # create subplots p = sns.scatterplot(data=v, x='x', y='y', hue='cat', palette=cmap) p.legend_.remove() # remove the individual plot legends plt.title(f'DataFrame: {k}') plt.tight_layout() # create legend from cmap patches = [Patch(color=v, label=k) for k, v in cmap.items()] # place legend outside of plot; change the right bbox value to move the legend up or down plt.legend(handles=patches, bbox_to_anchor=(1.06, 1.2), loc='center left', borderaxespad=0) plt.show() 

Option 2: How to 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, after adding a new identifier column, 'dataset', to each dataframe.
• This option is easier because it doesn't require manually mapping colors to 'cat'

Synthetic Data

data_list = list() for i in range(1, 7): np.random.seed(i) random.seed(i) data_length = 100 data = {'cat': [random.choice(['A', 'B', 'C']) for _ in range(data_length)], 'x': np.random.rand(data_length), 'y': np.random.rand(data_length), 'dataset': [i]*data_length} data_list.append(data) # convert each dict to a dataframe in a list-comprehension, and combine all the dataframes with concat df = pd.concat([pd.DataFrame(d) for d in data_list]).reset_index(drop=True) # display(df.head()) cat x y dataset 0 A 0.417022 0.326645 1 1 C 0.720324 0.527058 1 2 A 0.000114 0.885942 1 3 B 0.302333 0.357270 1 4 A 0.146756 0.908535 1 # display(df.tail()) cat x y dataset 595 C 0.315404 0.930243 6 596 C 0.863030 0.007895 6 597 C 0.558467 0.641872 6 598 C 0.546708 0.644663 6 599 B 0.367825 0.680251 6 

Plot a FacetGrid with seaborn.relplot

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

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) 

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)