I have two pandas series of numbers (not necessarily in the same size).
Can I create one side by side box plot for both of the series?
I didn't found a way to create a boxplot from a series, and not from 2 series.
For the test I generated 2 Series, of different size:
np.random.seed(0)
s1 = pd.Series(np.random.randn(10))
s2 = pd.Series(np.random.randn(14))
The first processing step is to concatenate them into a single DataFrame
and set some meaningful column names (will be included in the picture):
df = pd.concat([s1, s2], axis=1)
df.columns = ['A', 'B']
And to create the picture, along with a title, you can run:
ax = df.boxplot()
ax.get_figure().suptitle(t='My Boxplot', fontsize=16);
For my source data, the result is:
We can try with an example dataset, two series, unequal length, and defined colors.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
np.random.seed(100)
S1 = pd.Series(np.random.normal(0,1,10))
S2 = pd.Series(np.random.normal(0,1,14))
colors = ['#aacfcf', '#d291bc']
One option is to make a data.frame containing the two series in a column, and provide a label for the series:
fig, ax = plt.subplots(1, 1,figsize=(6,4))
import seaborn as sns
sns.boxplot(x='series',y='values',
data=pd.DataFrame({'values':pd.concat([S1,S2],axis=0),
'series':np.repeat(["S1","S2"],[len(S1),len(S2)])}),
ax = ax,palette=colors,width=0.5
)
The other, is to use matplotlib directly, as the other solutions have suggested. However, there is no need to concat them column wise and create some amounts of NAs. You can directly use plt.boxplot from matplotlib to plot an array of values. The downside is, that it takes a bit of effort to adjust the colors etc, as I show below:
fig, ax = plt.subplots(1, 1,figsize=(6,4))
bplot = ax.boxplot([S1,S2],patch_artist=True,widths=0.5,
medianprops=dict(color="black"),labels =['S1','S2'])
plt.setp(bplot['boxes'], color='black')
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
Try this:
import numpy as np
ser1 = pd.Series(np.random.randn(10))
ser2 = pd.Series(np.random.randn(10))
## solution
pd.concat([ser1, ser2], axis=1).plot.box()
Related
I have a multi index dataframe, with the two indices being Sample and Lithology
Sample 20EC-P 20EC-8 20EC-10-1 ... 20EC-43 20EC-45 20EC-54
Lithology Pd Di-Grd Gb ... Hbl Plag Pd Di-Grd Gb
Rb 7.401575 39.055118 6.456693 ... 0.629921 56.535433 11.653543
Ba 24.610102 43.067678 10.716841 ... 1.073115 58.520532 56.946630
Th 3.176471 19.647059 3.647059 ... 0.823529 29.647059 5.294118
I am trying to put it into a seaborn lineplot as such.
spider = sns.lineplot(data = data, hue = data.columns.get_level_values("Lithology"),
style = data.columns.get_level_values("Sample"),
dashes = False, palette = "deep")
The lineplot comes out as
1
I have two issues. First, I want to format hues by lithology and style by sample. Outside of the lineplot function, I can successfully access sample and lithology using data.columns.get_level_values, but in the lineplot they don't seem to do anything and I haven't figured out another way to access these values. Also, the lineplot reorganizes the x-axis by alphabetical order. I want to force it to keep the same order as the dataframe, but I don't see any way to do this in the documentation.
To use hue= and style=, seaborn prefers it's dataframes in long form. pd.melt() will combine all columns and create new columns with the old column names, and a column for the values. The index too needs to be converted to a regular column (with .reset_index()).
Most seaborn functions use order= to set an order on the x-values, but with lineplot the only way is to make the column categorical applying a fixed order.
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
column_tuples = [('20EC-P', 'Pd '), ('20EC-8', 'Di-Grd'), ('20EC-10-1 ', 'Gb'),
('20EC-43', 'Hbl Plag Pd'), ('20EC-45', 'Di-Grd'), ('20EC-54', 'Gb')]
col_index = pd.MultiIndex.from_tuples(column_tuples, names=["Sample", "Lithology"])
data = pd.DataFrame(np.random.uniform(0, 50, size=(3, len(col_index))), columns=col_index, index=['Rb', 'Ba', 'Th'])
data_long = data.melt(ignore_index=False).reset_index()
data_long['index'] = pd.Categorical(data_long['index'], data.index) # make categorical, use order of the original dataframe
ax = sns.lineplot(data=data_long, x='index', y='value',
hue="Lithology", style="Sample", dashes=False, markers=True, palette="deep")
ax.set_xlabel('')
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.02))
plt.tight_layout() # fit legend and labels into the figure
plt.show()
The long dataframe looks like:
index Sample Lithology value
0 Rb 20EC-P Pd 6.135005
1 Ba 20EC-P Pd 6.924961
2 Th 20EC-P Pd 44.270570
...
I have a list of case and control samples along with the information about what characteristics are present or absent in each of them. A dataframe including the information can be generated by Pandas:
import pandas as pd
df={'Patient':[True,True,False],'Control':[False,True,False]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
I need to visualize this data as a dotplot/scatterplot in the way that both of the x and y axis to be categorical and presence/absence to be coded by different shapes. Something like following:
Patient| x x -
Control| - x -
__________________
GeneA GeneB GeneC
I am new to Matplotlib/seaborn and I can plot simple line plots and scatter plots. But searching online I could not find any instructions or plot similar to what I need here.
A quick way would be:
import pandas as pd
import matplotlib.pyplot as plt
df={'Patient':[1,1,0],'Control':[0,1,0]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
heatmap = plt.imshow(df)
plt.xticks(range(len(df.columns.values)), df.columns.values)
plt.yticks(range(len(df.index)), df.index)
cbar = plt.colorbar(mappable=heatmap, ticks=[0, 1], orientation='vertical')
# vertically oriented colorbar
cbar.ax.set_yticklabels(['Absent', 'Present'])
Thanks to #DEEPAK SURANA for adding labels to the colorbar.
I searched the pyplot documentation and could not find a scatter or dot plot exactly like you described. Here is my take on creating a plot that illustrates what you want. The True records are blue and the False records are red.
# creating dataframe and extra column because index is not numeric
import pandas as pd
df={'Patient':[True,True,False],
'Control':[False,True,False]}
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
df['level'] = [i for i in range(0, len(df))]
print(df)
# plotting the data
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,6))
for idx, gene in enumerate(df.columns[:-1]):
df_gene = df[[gene, 'level']]
cList = ['blue' if x == True else 'red' for x in df[gene]]
for inr_idx, lv in enumerate(df['level']):
ax.scatter(x=idx, y=lv, c=cList[inr_idx], s=20)
fig.tight_layout()
plt.yticks([i for i in range(len(df.index))], list(df.index))
plt.xticks([i for i in range(len(df.columns)-1)], list(df.columns[:-1]))
plt.show()
Something like this might work
import pandas as pd
import numpy as np
from matplotlib.ticker import FixedLocator
df={'Patient':[1,1,0],'Control':[0,1,0]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
plot = df.T.plot()
loc = FixedLocator([0,1,2])
plot.xaxis.set_major_locator(loc)
plot.xaxis.set_ticklabels(df.columns)
look at https://matplotlib.org/examples/pylab_examples/major_minor_demo1.html
and https://matplotlib.org/api/ticker_api.html
I think you have to convert the boolean values to zeros and ones to make it work. Someting like df.astype(int)
I have two dataFrames that I would like to plot into a single graph. Here's a basic code:
#!/usr/bin/python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenarios = ['scen-1', 'scen-2']
for index, item in enumerate(scenarios):
df = pd.DataFrame({'A' : np.random.randn(4)})
print df
df.plot()
plt.ylabel('y-label')
plt.xlabel('x-label')
plt.title('Title')
plt.show()
However, this only plots the last dataFrame. If I use pd.concat() it plots one line with the combined values.
How can I plot two lines, one for the first dataFrame and one for the second one?
You need to put your plot in the for loop.
If you want them on a single plot then you need to use plot's ax kwarg to put them to plot on the same axis. Here I have created a fresh axis using subplots but this could be an already populated axis,
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenarios = ['scen-1', 'scen-2']
fig, ax = plt.subplots()
for index, item in enumerate(scenarios):
df = pd.DataFrame({'A' : np.random.randn(4)})
print df
df.plot(ax=ax)
plt.ylabel('y-label')
plt.xlabel('x-label')
plt.title('Title')
plt.show()
The plot function is only called once, and as you say this is with the last value of df. Put df.plot() inside the loop.
male[['Gender','Age']].plot(kind='hist', x='Gender', y='Age', bins=50)
female[['Gender','Age']].plot(kind='hist', x='Gender', y='Age', bins=50)
So basically, I used data from a file to create two histograms based on gender and age. From the beginning I separated the data by gender to initially plot. Now i'm having a hard time putting the two histograms together.
As mentioned in the comment, you can use matplotlib to do this task. I haven't figured out how to plot two histogram using Pandas tho (would like to see how people have done that).
import matplotlib.pyplot as plt
import random
# example data
age = [random.randint(20, 40) for _ in range(100)]
sex = [random.choice(['M', 'F']) for _ in range(100)]
# just give a list of age of male/female and corresponding color here
plt.hist([[a for a, s in zip(age, sex) if s=='M'],
[a for a, s in zip(age, sex) if s=='F']],
color=['b','r'], alpha=0.5, bins=10)
plt.show()
Consider converting the dataframes to a two-column numpy matrix as matplotlib's hist works with this structure instead of two different length pandas dataframes with non-numeric columns. Pandas' join is used to bind the two columns, MaleAge and FemaleAge.
Here, the Gender indicator is removed and manually labeled according to the column order.
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
...
# RESET INDEX AND RENAME COLUMN AFTER SUBSETTING
male = df2[df2['Gender'] == "M"].reset_index(drop=True).rename(columns={'Age':'MaleAge'})
female = df2[df2['Gender'] == "F"].reset_index(drop=True).rename(columns={'Age':'FemaleAge'})
# OUTER JOIN TO ACHIEVE SAME LENGTH
gendermat = np.array(male[['MaleAge']].join(female[['FemaleAge']], how='outer'))
plt.hist(gendermat, bins=50, label=['male', 'female'])
plt.legend(loc='upper right')
plt.show()
plt.clf()
plt.close()
tmpdf.boxplot(['original','new'], by = 'by column', ax = ax, sym = '')
gets me a plot like this
I want to compare "original" with "new", how can I arrange to put the two "0" boxes in one panel and the two "1" boxes in another panel? And of course swap the labelling with that.
Thanks
Here is a sample dataset to demonstrate.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# simulate some artificial data
# ==========================================
np.random.seed(0)
df = pd.DataFrame(np.random.rand(10,2), columns=['original', 'new'] )
df['by column'] = pd.Series([0,0,0,0,1,1,1,1,1,1])
# your original plot
ax = df.boxplot(['original', 'new'], by='by column', figsize=(12,6))
To get desired output, use groupby explicitly out of boxplot, so that we iterate over all subgroups, and plot a boxplot for each.
ax = df[['original', 'new']].groupby(df['by column']).boxplot(figsize=(12,6))