Data Wrangling in Python – How to Use Seaborn To Visualize A pandas Dataframe

Using Seaborn To Visualize A pandas Dataframe


import pandas as pd
%matplotlib inline
import random
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame()

df['x'] = random.sample(range(1, 100), 25)
df['y'] = random.sample(range(1, 100), 25)
x y
0 18 25
1 42 67
2 52 77
3 4 34
4 14 69


sns.lmplot('x', 'y', data=df, fit_reg=False)
<seaborn.axisgrid.FacetGrid at 0x114563b00>


Density Plot

<matplotlib.axes._subplots.AxesSubplot at 0x113ea2ef0>


sns.kdeplot(df.y, df.x)
<matplotlib.axes._subplots.AxesSubplot at 0x113d7fef0>


<matplotlib.axes._subplots.AxesSubplot at 0x114294160>



plt.hist(df.x, alpha=.3)



sns.boxplot([df.y, df.x])
<matplotlib.axes._subplots.AxesSubplot at 0x1142b8b38>


Violin Plot

sns.violinplot([df.y, df.x])
<matplotlib.axes._subplots.AxesSubplot at 0x114444a58>



sns.heatmap([df.y, df.x], annot=True, fmt="d")
<matplotlib.axes._subplots.AxesSubplot at 0x114530c88>



<seaborn.matrix.ClusterGrid at 0x116f313c8>


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