CODEWITHSUNDEEP
pythonpandasboxplot
user1877600
user1877600

Reputation: 647

How do I create a Box plot for each column in a Pandas Dataframe?

My data frames (pandas's structure) looks like above enter image description here

Now I want to make boxplot for each feature on separate canvas. The separation condition is the first column. I have similar plot for histogram (code below) but I can't make working version for the boxplot. 

 hist_params = {'normed': True, 'bins': 60, 'alpha': 0.4}
# create the figure
fig = plt.figure(figsize=(16,  25))
for n, feature in enumerate(features):
    # add sub plot on our figure
    ax = fig.add_subplot(features.shape[1] // 5 + 1, 6, n + 1)
    # define range for histograms by cutting 1% of data from both ends
    min_value, max_value = numpy.percentile(data[feature], [1, 99])
    ax.hist(data.ix[data.is_true_seed.values == 0, feature].values, range=(min_value, max_value), 
             label='ghost', **hist_params)
    ax.hist(data.ix[data.is_true_seed.values == 1, feature].values, range=(min_value, max_value), 
             label='true', **hist_params)
    ax.legend(loc='best')

    ax.set_title(feature)

Above code produce such output as (attached only part of it): enter image description here

Upvotes: 7

Views: 25025

Answers (3)

m13op22
m13op22

Reputation: 2337

If you want to use Seaborn to plot them all in one figure, you can melt the data frame and then plot it.

import numpy as np
import pandas as pd
import seaborn as sns

df = pd.DataFrame({'is_true_seed': np.random.choice([True, False], 10),
                   'col1': np.random.normal(size=10),
                   'col2': np.random.normal(size=10),
                   'col3': np.random.normal(size=10)})

sns.boxplot(data = df.melt(), x = 'variable', y = 'value')

enter image description here

Upvotes: 1

helcode
helcode

Reputation: 2048

Linking my answer from another related question

If you want to create a separate plot per column, then you can iterate over each column and use plt.figure() to initiate a new figure for each plot.

import matplotlib.pyplot as plt

for column in df:
    plt.figure()
    df.boxplot([column])

If you want to just put all columns into the same boxplot graph then you can just use df.plot(kind='box')

Upvotes: 1

Alicia Garcia-Raboso
Alicia Garcia-Raboso

Reputation: 13923

DataFrame.boxplot() automates this rather well:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'is_true_seed': np.random.choice([True, False], 10),
                   'col1': np.random.normal(size=10),
                   'col2': np.random.normal(size=10),
                   'col3': np.random.normal(size=10)})

   is_true_seed      col1      col2      col3
0         False -0.990041 -0.561413 -0.512582
1         False  0.825099  0.827453 -0.366211
2          True  0.083442 -1.199540  0.345792
3          True  0.065715  1.560029 -0.324501
4          True -1.699770 -0.270820 -1.380125

ax = df.boxplot(['col1', 'col2', 'col3'], 'is_true_seed', figsize=(10,  10))

enter image description here

The first argument tells pandas which columns to plot, the second which column to group by (what you call the separation condition), and the third on which axes to draw.

Listing all columns but the one you want to group by can get tedious, but you can avoid it by omitting that first argument. You then have to explicitly name the other two:

ax = df.boxplot(by='is_true_seed', figsize=(10,  10))

Upvotes: 11

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