如何将单独的 Pandas DataFrames 绘制为子图？

Question

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.

Answer 1

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 .

Answer 2

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 .

Answer 3

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...

Answer 4

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.

Answer 5

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()

Answer 6

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()

Answer 7

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)

Answer 8

How to create multiple plots from a dictionary of dataframes with long (tidy) data

• Assumptions:

  • There is a dictionary of multiple dataframes of tidy data
    • Created by reading in from files
    • Created by separating a single dataframe into multiple dataframes
  • The categories, cat , may be overlapping, but all dataframes may not contain all values of cat
  • hue='cat'

• Because dataframes are being iterated through, there's not 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()

Answer 9

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)

Answer 10

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)