I wrote the basic operation of Seaborn in Jupyter Lab

24th

Seaborn

import seaborn as sns
%matplotlib inline

import pandas as pd
df = pd.read_csv('train.csv')
df = df.dropna(subset=['Age'])
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd7e3d0>

sns.set()
sns.distplot(df['Age'],bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd05850>

See the distribution of two variables with sns.jointplot ()

sns.jointplot(x='Age', y='Fare', data=df)

<seaborn.axisgrid.JointGrid at 0x7fcbcdbe31d0>

sns.jointplot(x='Age', y='Fare', data=df, kind='hex')

<seaborn.axisgrid.JointGrid at 0x7fcbcdb2b890>

(Important) Display the distribution of multiple columns in one shot with sns.pairplot ()

sns.pairplot(df[['Age', 'Fare', 'Pclass', 'Survived']], hue='Survived', kind='scatter', plot_kws={'alpha': 0.5})

/opt/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:487: RuntimeWarning: invalid value encountered in true_divide
  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)
/opt/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars
  FAC1 = 2*(np.pi*bw/RANGE)**2





<seaborn.axisgrid.PairGrid at 0x7fcbcda34990>

25th

sns.barplot () Make a "bar chart"

import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv('train.csv')
df.head()

sns.barplot(x='Survived', y='Age', data=df) #Average value

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd7e390>

sns.barplot(x='Survived', y='Age', data=df, estimator=np.median) #Median

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcce3a190>

sns.countplot () Compare the number of data

sns.countplot(x='Sex', data=df, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbccfb0c10>

sns.boxplot () Compare values by category

sns.boxplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbccd82750>

sns.boxplot(x='Pclass', y='Age', data=df, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc2a4ed0>

sns.violonplot () Visualize data analysis

sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc1e3ad0>

sns.violinplot(x='Pclass', y='Age', data=df, hue=('Survived'))

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc107210>

sns.swarmplot () You can see the true distribution

sns.swarmplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc19a710>

sns.swarmplot(x='Pclass', y='Age', data=df, size=4, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc6e9fe50>

26th

Draw a Heatmap

Make a correlation with df.corr ()

import pandas as pd
df = pd.read_csv('train.csv')
corr = df.corr()
corr

plot Heatmap with sns.heatmap ()

sns.heatmap(corr)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc6df9850>

sns.heatmap(corr, cmap='coolwarm', annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcd0b3290>

Sns.heatmap () to help you get a bird's eye view of your data

flights = sns.load_dataset('flights')
print(len(flights))
flights.head()

144

# pivot_Create table
flights_pivot = flights.pivot_table(index='month', columns='year', values='passengers') 
flights_pivot

sns.heatmap(flights_pivot)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc5baabd0>

27th

Change basic style with sns.set ()

Specify the usage with the context argument

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv('train.csv')

sns.set(context=('poster'))
df = df.dropna(subset=['Age'])
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc568f690>

Specify the style of the entire graph with the style argument

sns.set_style(style='whitegrid') #Change background color
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc563d850>

Specify color with palette argument

sns.set(palette='bright')
sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc5472850>

Drop axes and frames with sns.despine ()

sns.set(palette='bright' ,style='ticks') ##style='ticks'With axis to plot
sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc53ed810>

sns.set(palette='bright' )
sns.violinplot(x='Pclass', y='Age', data=df)
sns.despine()

Like matplotlib, you can do many things with the plt module

import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc52d8750>

sns.distplot(df['Age'])
plt.savefig('seaborn_sample.png')