As an exercise, I'm reproducing a plot from The Economist with matplotlib
So far, I can generate a random data and produce two plots independently. I'm struggling now with putting them next to each other horizontally.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
df1.plot.bar(stacked=True)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
plt.figure()
fig, ax1 = plt.subplots()
ax1.plot(df2['avg_hour'])
ax2 = ax1.twinx()
ax2.bar(left=range(2009,2016),height=df2['usage'])
plt.show()
You should try using subplots. First you create a figure by plt.figure(). Then add one subplot(121) where 1 is number of rows, 2 is number of columns and last 1 is your first plot. Then you plot the first dataframe, note that you should use the created axis ax1. Then add the second subplot(122) and repeat for the second dataframe. I changed your axis ax2 to ax3 since now you have three axis on one figure. The code below produces what I believe you are looking for. You can then work on aesthetics of each plot separately.
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
ax1 = fig.add_subplot(121)
df1.plot.bar(stacked=True,ax=ax1)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
ax2 = fig.add_subplot(122)
ax2.plot(df2['avg_hour'])
ax3 = ax2.twinx()
ax3.bar(left=range(2009,2016),height=df2['usage'])
plt.show()
Related
I've been playing with Titanic dataset and working through some visualisations in Pandas using this tutorial. https://www.kdnuggets.com/2023/02/5-pandas-plotting-functions-might-know.html
I have a visual of scatterplot having used this code.
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('train.csv')
I was confused by bootstrap plot result so went on to scatterplot.
pd.plotting.scatter_matrix(df, figsize=(10,10), )
plt.show()
I can sort of interpret it but I'd like to put the various variables at top and bottom of every column. Is that doable?
You can use:
fig, ax = plt.subplots(4, 3, figsize=(20, 15))
sns.scatterplot(x = 'bedrooms', y = 'price', data = dataset, whis=1.5, ax=ax[0, 0])
sns.scatterplot(x = 'bathrooms', y = 'price', data = dataset, whis=1.5, ax=ax[0, 1])
I have a data frame with four columns I would like to plot the normality test for each column in a 2*2 grid, but it only plot one figure, and the else is empty.
import random
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2,2, figsize=(15, 6), facecolor='w', edgecolor='k')
fig.subplots_adjust(hspace = .5, wspace=.001)
data = {'col1': [random.randrange(1, 50, 1) for i in range(1000)], 'col2': [random.randrange(1, 50, 1) for i in range(1000)],'col3':[random.randrange(1, 50, 1) for i in range(1000)]
,'col4':[random.randrange(1, 50, 1) for i in range(1000)]}
df = pd.DataFrame(data)
for ax, d in zip(axs.ravel(), df):
ax=stats.probplot(df[d], plot=plt)
#ax.set_title(str(d))
plt.show()
is there a way to construct the subplot and the stats.probplot within a loop?
In your code, you need to change the for loop to this:
for ax, d in zip(axs.ravel(), df):
stats.probplot(df[d], plot=ax)
#ax.set_titl(str(d))
plt.show()
I hope this will help you move on.
I'm trying to get a line plot to be over the bar plot. But no matter what I do to change the zorder, it seems like it keeps the bar on top of the line. Nothing I do to try to change zorder seems to work. Sometimes the bar plot just doesn't show up if zorder is <= 0.
import pandas as pd
import matplotlib.pyplot as plt
def tail_plot(tail):
plt.figure()
#line plot
ax1 = incidence[incidence['actual_inc'] != 0].tail(tail).plot(x='date', y=['R_t', 'upper 95% CI', 'lower 95% CI'], color = ['b', '#808080', '#808080'])
ax1.set_zorder(2)
ax2 = ax1.twinx()
inc = incidence[incidence['actual_inc'] != 0]['actual_inc'].tail(tail).values
dates = incidence[incidence['actual_inc'] != 0]['date'].tail(tail).values
#bar plot
ax2.bar(dates, inc, color ='red', zorder=1)
ax2.set_zorder(1)
Keeps giving me this:
The problem with the approach in the post is that ax1 has a white background which totally occludes the plot of ax2. To solve this, the background color can be set to 'none'.
Note that the plt.figure() in the example code of the post creates an empty plot because the pandas plot creates its own new figure (as no ax is given explicitly).
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({f'curve {i}': 20 + np.random.normal(.1, .5, 30).cumsum() for i in range(1, 6)})
# line plot
ax1 = df.plot()
ax1.set_zorder(2)
ax1.set_facecolor('none')
ax2 = ax1.twinx()
# bar plot
x = np.arange(30)
ax2.bar(x, np.random.randint(7 + x, 2 * x + 10), color='red', zorder=1)
ax2.set_zorder(1)
plt.show()
I have a pandas series containing numbers ranging between 0 and 100. I want to visualise it in a horizontal bar consisting of 3 main colours.
I have tried using seaborn but all I can get is a heatmap matrix. I have also tried the below code, which is producing what I need but not in the way I need it.
x = my_column.values
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='brg')
ax2.scatter(x, y, c=t, cmap='brg')
plt.show()
What I'm looking for is something similar to the below figure, how can I achieve that using matplotlib or seaborn?
The purpose of this is not quite clear, however, the following would produce an image like the one shown in the question:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
x = np.linspace(100,0,101)
fig, ax = plt.subplots(figsize=(6,1), constrained_layout=True)
cmap = LinearSegmentedColormap.from_list("", ["limegreen", "gold", "crimson"])
ax.imshow([x], cmap=cmap, aspect="auto",
extent=[x[0]-np.diff(x)[0]/2, x[-1]+np.diff(x)[0]/2,0,1])
ax.tick_params(axis="y", left=False, labelleft=False)
plt.show()
I am plotting a figure with 6 sets of axes, each with a series of 3 lines from one of 2 Pandas dataframes (1 line per column).
I have been using matplotlib .plot:
import pandas as pd
import matplotlib.pyplot as plt
idx = pd.DatetimeIndex(start = '2013-01-01 00:00', periods =24,freq = 'H')
df1 = pd.DataFrame(index = idx, columns = ['line1','line2','line3'])
df1['line1']= df1.index.hour
df1['line2'] = 24 - df1['line1']
df1['line3'] = df1['line1'].mean()
df2 = df1*2
df3= df1/2
df4= df2+df3
fig, ax = plt.subplots(2,2,squeeze=False,figsize = (10,10))
ax[0,0].plot(df1.index, df1, marker='', linewidth=1, alpha=1)
ax[0,1].plot(df2.index, df2, marker='', linewidth=1, alpha=1)
ax[1,0].plot(df3.index, df3, marker='', linewidth=1, alpha=1)
ax[1,1].plot(df4.index, df4, marker='', linewidth=1, alpha=1)
fig.show()
It's all good, and matplotlib automatically cycles through a different colour for each line, but uses the same colours for each plot, which is what i wanted.
However, now I want to specify more details for the lines: choosing specific colours for each line, and / or changing the linestyle for each line.
This link shows how to pass multiple linestyles to a Pandas plot. e.g. using
ax = df.plot(kind='line', style=['-', '--', '-.'])
So I need to either:
pass lists of styles to my subplot command above, but style is not recognised and it doesn't accept a list for linestyle or color. Is there a way to do this?
or
Use df.plot:
fig, ax = plt.subplots(2,2,squeeze=False,figsize = (10,10))
ax[0,0] = df1.plot(style=['-','--','-.'], marker='', linewidth=1, alpha=1)
ax[0,1] = df2.plot(style=['-','--','-.'],marker='', linewidth=1, alpha=1)
ax[1,0] = df3.plot( style=['-','--','-.'],marker='', linewidth=1, alpha=1)
ax[1,1] = df4.plot(style=['-','--','-.'], marker='', linewidth=1, alpha=1)
fig.show()
...but then each plot is plotted as a seperate figure. I can't see how to put multiple Pandas plots on the same figure.
How can I make either of these approaches work?
using matplotlib
Using matplotlib, you may define a cycler for the axes to loop over color and linestyle automatically. (See this answer).
import numpy as np; np.random.seed(1)
import pandas as pd
import matplotlib.pyplot as plt
f = lambda i: pd.DataFrame(np.cumsum(np.random.randn(20,3),0))
dic1= dict(zip(range(3), [f(i) for i in range(3)]))
dic2= dict(zip(range(3), [f(i) for i in range(3)]))
dics = [dic1,dic2]
rows = range(3)
def set_cycler(ax):
ax.set_prop_cycle(plt.cycler('color', ['limegreen', '#bc15b0', 'indigo'])+
plt.cycler('linestyle', ["-","--","-."]))
fig, ax = plt.subplots(3,2,squeeze=False,figsize = (8,5))
for x in rows:
for i,dic in enumerate(dics):
set_cycler(ax[x,i])
ax[x,i].plot(dic[x].index, dic[x], marker='', linewidth=1, alpha=1)
plt.show()
using pandas
Using pandas you can indeed supply a list of possible colors and linestyles to the df.plot() method. Additionally you need to tell it in which axes to plot (df.plot(ax=ax[i,j])).
import numpy as np; np.random.seed(1)
import pandas as pd
import matplotlib.pyplot as plt
f = lambda i: pd.DataFrame(np.cumsum(np.random.randn(20,3),0))
dic1= dict(zip(range(3), [f(i) for i in range(3)]))
dic2= dict(zip(range(3), [f(i) for i in range(3)]))
dics = [dic1,dic2]
rows = range(3)
color = ['limegreen', '#bc15b0', 'indigo']
linestyle = ["-","--","-."]
fig, ax = plt.subplots(3,2,squeeze=False,figsize = (8,5))
for x in rows:
for i,dic in enumerate(dics):
dic[x].plot(ax=ax[x,i], style=linestyle, color=color, legend=False)
plt.show()