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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.
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 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()
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 figue and axes
fig, axes = plt.subplots(row_nums, col_nums, figsize=(9, 6), sharex=True, sharey=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)
fig.tight_layout()
# 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
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)
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)
I am trying to create multiple box plot charts for about 5 columns in my dataframe (df_summ):
columns = ['dimension_a','dimension_b']
for i in columns:
sns.set(style = "ticks", palette = "pastel")
box_plot = sns.boxplot(y="measure", x=i,
palette=["m","g"],
data=df_summ_1500_delta)
sns.despine(offset=10, trim=True)
medians = df_summ_1500_delta.groupby([i])['measure'].median()
vertical_offset=df_summ_1500_delta['measure'].median()*-0.5
for xtick in box_plot.get_xticks():
box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick],
horizontalalignment='center',size='small',color='blue',weight='semibold')
My only issue is that they aren't be separated on different facets, but rather on top of each other.
Any help on how I can make both on their own separate chart with the x axis being 'dimension a' and the x axis of the second chart being 'dimension b'.
To draw two boxplots next to each other at each x-position, you can use a hue for dimension_a and dimension_b separately. These two columns need to be transformed (with pd.melt()) to "long form".
Here is a some example code starting from generated test data. Note that the order both for the x-values as for the hue-values needs to be enforced to be sure of their exact position. The individual box plots are distributed over a width of 0.8.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
df = pd.DataFrame({'dimension_a': np.random.choice(['hot', 'cold'], 100),
'dimension_b': np.random.choice(['hot', 'cold'], 100),
'measure': np.random.uniform(100, 500, 100)})
df.loc[df['dimension_a'] == 'hot', 'measure'] += 100
df.loc[df['dimension_a'] == 'cold', 'measure'] -= 100
x_order = ['hot', 'cold']
columns = ['dimension_a', 'dimension_b']
df1 = df.melt(value_vars=columns, var_name='dimension', value_name='value', id_vars='measure')
sns.set(style="ticks", palette="pastel")
ax = sns.boxplot(data=df1, x='value', order=x_order, y='measure',
hue='dimension', hue_order=columns, palette=["m", "g"], dodge=True)
ax.set_xlabel('')
sns.despine(offset=10, trim=True)
for col, dodge_dist in zip(columns, np.linspace(-0.4, 0.4, 2 * len(x_order) + 1)[1::2]):
medians = df.groupby([col])['measure'].median()
vertical_offset = df['measure'].median() * -0.5
for x_ind, xtick in enumerate(x_order):
ax.text(x_ind + dodge_dist, medians[xtick] + vertical_offset, f'{medians[xtick]:.2f}',
horizontalalignment='center', size='small', color='blue', weight='semibold')
plt.show()
I want to plot multiple seaborn distplot under a same window, where each plot has the same x and y grid. My attempt is shown below, which does not work.
# function to plot the density curve of the 200 Median Stn. MC-losses
def make_density(stat_list,color, layer_num):
num_subplots = len(stat_list)
ncols = 3
nrows = (num_subplots + ncols - 1) // ncols
fig, axes = plt.subplots(ncols=ncols, nrows=nrows, figsize=(ncols * 6, nrows * 5))
for i in range(len(stat_list)):
# Plot formatting
plt.title('Layer ' + layer_num)
plt.xlabel('Median Stn. MC-Loss')
plt.ylabel('Density')
plt.xlim(-0.2,0.05)
plt.ylim(0, 85)
min_ylim, max_ylim = plt.ylim()
# Draw the density plot.
sns.distplot(stat_list, hist = True, kde = True,
kde_kws = {'linewidth': 2}, color=color)
# `stat_list` is a list of 6 lists
# I want to draw histogram and density plot of
# each of these 6 lists contained in `stat_list` in a single window,
# where each row containing the histograms and densities of the 3 plots
# so in my example, there would be 2 rows of 3 columns of plots (2 x 3 =6).
stat_list = [[0.3,0.5,0.7,0.3,0.5],[0.2,0.1,0.9,0.7,0.4],[0.9,0.8,0.7,0.6,0.5]
[0.2,0.6,0.75,0.87,0.91],[0.2,0.3,0.8,0.9,0.3],[0.2,0.3,0.8,0.87,0.92]]
How can I modify my function to draw multiple distplot under the same window, where the x and y grid for each displayed plot is identical?
Thank you,
PS: Aside, I want the 6 distplots to have identical color, preferably green for all of them.
The easiest method is to load the data into pandas and then use seaborn.displot.
.displot replaces .distplot in seaborn version 0.11.0
Technically, what you would have wanted before, is a FacetGrid mapped with distplot.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# data
stat_list = [[0.3,0.5,0.7,0.3,0.5], [0.2,0.1,0.9,0.7,0.4], [0.9,0.8,0.7,0.6,0.5], [0.2,0.6,0.75,0.87,0.91], [0.2,0.3,0.8,0.9,0.3], [0.2,0.3,0.8,0.87,0.92]]
# load the data into pandas and then transpose it for the correct column data
df = pd.DataFrame(stat_list).T
# name the columns; specify a layer number
df.columns = ['A', 'B', 'C', 'D', 'E', 'F']
# now stack the data into a long (tidy) format
dfl = df.stack().reset_index(level=1).rename(columns={'level_1': 'Layer', 0: 'Median Stn. MC-Loss'})
# plot a displot
g = sns.displot(data=dfl, x='Median Stn. MC-Loss', col='Layer', col_wrap=3, kde=True, color='green')
g.set_axis_labels(y_var='Density')
g.set(xlim=(0, 1.0), ylim=(0, 3.0))
sns.FacetGrid and sns.distplot
.distplot is deprecated
p = sns.FacetGrid(data=dfl, col='Layer', col_wrap=3, height=5)
p.map(sns.distplot, 'Median Stn. MC-Loss', bins=5, kde=True, color='green')
p.set(xlim=(0, 1.0))
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.
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 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()
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 figue and axes
fig, axes = plt.subplots(row_nums, col_nums, figsize=(9, 6), sharex=True, sharey=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)
fig.tight_layout()
# 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
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)
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)
I can't get the legends to show on the subplots which show up just fine and take the other formatting I've applied. What am I missing?
If I do a plot for the dataframe alone, it shows the legend. If I add a label to the plot for the subplots, it assigns that label to all three lines.
Here is image. plot vs subplot
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from functools import reduce
%matplotlib notebook
#Source for files
# Per Capita Personal Income
# Ann Arbor https://fred.stlouisfed.org/series/ANNA426PCPI
# MI https://fred.stlouisfed.org/series/MIPCPI
# USA https://fred.stlouisfed.org/series/A792RC0A052NBEA
dfAnnArbor_PCPI = pd.read_csv('PerCapitaPersonalIncomeAnnArborMI.csv', skiprows=1, names=['Date', 'PCPI'])
dfMI_PCPI = pd.read_csv('PerCapitaPersonalIncomeMI.csv', skiprows=1, names=['Date', 'PCPI'])
dfUSA_PCPI = pd.read_csv('PerCapitaPersonalIncomeUSA.csv', skiprows=1, names=['Date', 'PCPI'])
# consolidate three df into one using Date
dfAll = [dfAnnArbor_PCPI, dfMI_PCPI, dfUSA_PCPI]
dfPCPI = reduce(lambda left, right: pd.merge(left, right, on='Date', how='outer'), dfAll)
dfPCPI = dfPCPI.dropna() # drop rows with NaN
dfPCPI.columns = ['Date', 'AnnArbor', 'MI', 'USA'] # rename columns
dfPCPI['Date'] = dfPCPI['Date'].str[:4] # select only year
dfPCPI = dfPCPI.set_index('Date')
dfPCPI_Rel = dfPCPI.apply(lambda x: x / x[0])
dfPCPI_Small = dfPCPI.iloc[8:].copy()
dfPCPI_SmRel = dfPCPI_Small.apply(lambda x: x / x[0])
dfPCPI_SmRel.plot()
fig, ax = plt.subplots(1, 2)
ax0 = ax[0].plot(dfPCPI_Rel, '-', label='a')
ax1 = ax[1].plot(dfPCPI_SmRel, '-', label='test1')
ax[0].legend()
for x in fig.axes:
for label in x.get_xticklabels():
label.set_rotation(45)
ax[1].xaxis.set_major_locator(ticker.MultipleLocator(2))
plt.show()
The legend in pyplot refers to an axis instance. Therefore, if you want multiple plots to have their own legend, you need to call legend() for each axis. In your case
ax[0].legend()
ax[1].legend()
Additionally, as you are calling plot(), you may want to use the keyword label in each plot() call so as to have a label for each legend entry.
You should try fig.legend() instead of plt.legend()