[PYTHON] 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'])

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

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

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

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

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(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





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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

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

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sns.countplot () Compare the number of data

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

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sns.boxplot () Compare values by category

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

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

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sns.violonplot () Visualize data analysis

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

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

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sns.swarmplot () You can see the true distribution

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

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

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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)

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

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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)

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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'])

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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'])

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