Related
I have two bars which I want to mirror. I have the following code
bar1 = df['nt'].value_counts().plot.barh()
bar2 = df1['nt'].value_counts().plot.barh()
bar1.set_xlim(bar1.get_xlim()[::-1])
# bar1.yaxis.tick_right()
But somehow not only the bar1 flips to the left(third line), but also the bar2. The same happening with the commented 4th line. Why is that? How to do it right then?
df...plot.barh()doesn't return bars nor a barplot. It returns theaxwhich indicates the subplot where the barplot was added. As both barplots are created onto the same subplot,set_xlim` etc. will act on that same subplot. This blogpost might be helpful.
To get two barplots, one from the left and one from the right, you could create a "twin" y -axis and then drawing one bar plot using the lower x-axis and the other user the upper x-axis. To make things clearer, the tick labels can be colored the same as the bars. To avoid overlapping bars, the x limits should be at least the maximum of the sum of the two value_counts.
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
df = pd.DataFrame({'nt': np.random.choice([*'abcdefhij'], 50)})
df1 = pd.DataFrame({'nt': np.random.choice([*'abcdefhij'], 50)})
max_sum_value_counts = df.append(df1).value_counts().max()
fig, ax = plt.subplots(figsize=(12, 5))
df['nt'].value_counts(sort=False).sort_index().plot.barh(ax=ax, color='purple')
ax.set_xlim(0, max_sum_value_counts + 1)
ax.tick_params(labelcolor='purple')
ax1 = ax.twiny()
df1['nt'].value_counts(sort=False).sort_index().plot.barh(ax=ax1, color='crimson')
ax1.set_xlim(max_sum_value_counts + 1, 0)
ax1.tick_params(labelcolor='crimson', labelright=True, labelleft=False)
ax1.invert_yaxis()
plt.show()
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 using MatLibPlot to fetch data from an excel file and to create a scatter plot.
Here is a minimal sample table
In my scatter plot, I have two sets of XY values. In both sets, my X values are country population. I have Renewable Energy Consumed as my Y value in one set and Non-Renewable Energy Consumed in the other set.
For each Country, I would like to have a line from the renewable point to the non-renewable point.
My example code is as follows
import pandas as pd
import matplotlib.pyplot as plt
excel_file = 'example_graphs.xlsx'
datasheet = pd.read_excel(excel_file, sheet_name=0, index_col=0)
ax = datasheet.plot.scatter("Xcol","Y1col",c="b",label="set_one")
datasheet.scatter("Xcol","Y2col",c="r",label="set_two", ax=ax)
ax.show()
And it produces the following plot
I would love to be able to draw a line between the two sets of points, preferably a line I can change the thickness and color of.
As commented, you could simply loop over the dataframe and plot a line for each row.
import pandas as pd
import matplotlib.pyplot as plt
datasheet = pd.DataFrame({"Xcol" : [1,2,3],
"Y1col" : [25,50,75],
"Y2col" : [75,50,25]})
ax = datasheet.plot.scatter("Xcol","Y1col",c="b",label="set_one")
datasheet.plot.scatter("Xcol","Y2col",c="r",label="set_two", ax=ax)
for n,row in datasheet.iterrows():
ax.plot([row["Xcol"]]*2,row[["Y1col", "Y2col"]], color="limegreen", lw=3, zorder=0)
plt.show()
I'm having trouble understanding Pandas subplots - and how to create axes so that all subplots are shown (not over-written by subsequent plot).
For each "Site", I want to make a time-series plot of all columns in the dataframe.
The "Sites" here are 'shark' and 'unicorn', both with 2 variables. The output should be be 4 plotted lines - the time-indexed plot for Var 1 and Var2 at each site.
Make Time-Indexed Data with Nans:
df = pd.DataFrame({
# some ways to create random data
'Var1':pd.np.random.randn(100),
'Var2':pd.np.random.randn(100),
'Site':pd.np.random.choice( ['unicorn','shark'], 100),
# a date range and set of random dates
'Date':pd.date_range('1/1/2011', periods=100, freq='D'),
# 'f':pd.np.random.choice( pd.date_range('1/1/2011', periods=365,
# freq='D'), 100, replace=False)
})
df.set_index('Date', inplace=True)
df['Var2']=df.Var2.cumsum()
df.loc['2011-01-31' :'2011-04-01', 'Var1']=pd.np.nan
Make a figure with a sub-plot for each site:
fig, ax = plt.subplots(len(df.Site.unique()), 1)
counter=0
for site in df.Site.unique():
print(site)
sitedat=df[df.Site==site]
sitedat.plot(subplots=True, ax=ax[counter], sharex=True)
ax[0].title=site #Set title of the plot to the name of the site
counter=counter+1
plt.show()
However, this is not working as written. The second sub-plot ends up overwriting the first. In my actual use case, I have 14 variable number of sites in each dataframe, as well as a variable number of 'Var1, 2, ...'. Thus, I need a solution that does not require creating each axis (ax0, ax1, ...) by hand.
As a bonus, I would love a title of each 'site' above that set of plots.
The current code over-writes the first 'Site' plot with the second. What I missing with the axes here?!
When you are using DataFrame.plot(..., subplot=True) you need to provide the correct number of axes that will be used for each column (and with the right geometry, if using layout=). In your example, you have 2 columns, so plot() needs two axes, but you are only passing one in ax=, therefore pandas has no choice but to delete all the axes and create the appropriate number of axes itself.
Therefore, you need to pass an array of axes of length corresponding to the number of columns you have in your dataframe.
# the grouper function is from itertools' cookbook
from itertools import zip_longest
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
fig, axs = plt.subplots(len(df.Site.unique())*(len(df.columns)-1),1, sharex=True)
for (site,sitedat),axList in zip(df.groupby('Site'),grouper(axs,len(df.columns)-1)):
sitedat.plot(subplots=True, ax=axList)
axList[0].set_title(site)
plt.tight_layout()
I have two dataframe with the same columns but different content.
I have plotted dffinal data frame. now I want to plot another dataframe dffinal_no on the same diagram to be comparable.
for example one bar chart in blue colour, and the same bar chart with another colour just differentiating in y-axis.
This is part of the code in which I have plotted the first data frame.
dffinal = df[['6month','final-formula','numPatients6month']].drop_duplicates().sort_values(['6month'])
ax=dffinal.plot(kind='bar',x='6month', y='final-formula')
import matplotlib.pyplot as plt
ax2 = ax.twinx()
dffinal.plot(ax=ax2,x='6month', y='numPatients6month')
plt.show()
Now imagine I have another dffinal_no data frame with the same columns, how can I plot it in the same diagram?
This is my first diagram which I plotted, I want the other bar chart on this diagram with another color.
so the answer of #Mohamed Thasin ah is somehow what I want, except that the right y-axis is not correct.
I want both data frame be based on (6month, final-formula) but the right y-axis is just showing number of patients, as an information for the user.
Actually, I DO NOT want the first df based on final-fomula and the second df be based on NumberPatients.
Update1 jast as a refrence how it looks like my data frame
dffinal = df[['6month','final-formula','numPatients6month']].drop_duplicates().sort_values(['6month'])
nocidffinal = nocidf[['6month','final-formula','numPatients6month']].drop_duplicates().sort_values(['6month'])
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylabel('final-formula')
ax2.set_ylabel('numPatients6month')
width=0.4
nocidffinal=nocidffinal.set_index('6month').sort_index()
dffinal=dffinal.set_index('6month').sort_index()
nocidffinal['final-formula'].plot(kind='bar',color='green',ax=ax1,width=width,position=0)
dffinal['numPatients6month'].plot(kind='bar',color='red',ax=ax2,width=width,position=1)
dffinal content
,6month,final-formula,numPatients6month
166047.0,1,7.794117647058823,680
82972.0,2,5.720823798627003,437
107227.0,3,5.734767025089606,558
111330.0,4,4.838709677419355,434
95591.0,5,3.3707865168539324,534
95809.0,6,3.611738148984198,443
98662.0,7,3.5523978685612785,563
192668.0,8,2.9978586723768736,467
89460.0,9,0.9708737864077669,515
192585.0,10,2.1653543307086616,508
184325.0,11,1.727447216890595,521
85068.0,12,1.0438413361169103,479
nocidffinal
,6month,final-formula,numPatients6month
137797.0,1,3.5934291581108826,974
267492.0,2,2.1705426356589146,645
269542.0,3,2.2106631989596877,769
271950.0,4,2.0,650
276638.0,5,1.5587529976019185,834
187719.0,6,1.9461077844311379,668
218512.0,7,1.1406844106463878,789
199830.0,8,0.8862629246676514,677
269469.0,9,0.3807106598984772,788
293390.0,10,0.9668508287292817,724
254783.0,11,1.2195121951219512,738
300974.0,12,0.9695290858725761,722
to compare two data frame result with bar plot one way you could try is concatenating two data frames and adding hue.
For example consider below df it contains same x and y columns in both df's and wanna compare this values. to achieve this simply add hue column for each df with differentiating constant like below.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df1=pd.DataFrame({'x':[1,2,3,4,5],'y':[10,2,454,121,34]})
df2=pd.DataFrame({'x':[4,1,2,5,3],'y':[54,12,65,12,8]})
df1['hue']=1
df2['hue']=2
res=pd.concat([df1,df2])
sns.barplot(x='x',y='y',data=res,hue='hue')
plt.show()
The result should looks like below:
To get two y-axis try this method,
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylabel('final-formula')
ax2.set_ylabel('numPatients6month')
width=0.4
df1=df1.set_index('x').sort_index()
df2=df2.set_index('x').sort_index()
df1['y'].plot(kind='bar',color='blue',ax=ax1,width=width,position=1)
df2['y'].plot(kind='bar',color='green',ax=ax2,width=width,position=0)
plt.show()
with actual input:
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylabel('final-formula')
ax2.set_ylabel('numPatients6month')
width=0.4
df1=df1.set_index('6month').sort_index()
df2=df2.set_index('6month').sort_index()
df1['final-formula'].plot(kind='bar',color='blue',ax=ax1,width=width,position=1)
df2['numPatients6month'].plot(kind='bar',color='green',ax=ax2,width=width,position=0)
plt.show()