Seaborn

0]Countplot [aama x axix pr name che aene cross ma lkhva tay che]
sns.countplot(x='Type 1',data=df,palette='rainbow')
plt.xticks(rotation=70)
plt.rcParams['xtick.labelsize'] = 15

plt.rcParams['axes.labelsize'] = 20




01]ax=df[1:50].plot.area(alpha=0.4, figsize=(5,4))
     ax.legend(bbox_to_anchor=(1.0,1.0))
[aama instant,season,yr,mnth,hr........graph ni bhar lkhva mate
use tay che ax.legenda vadi line add karvi]

1] sns.barplot(x='sex',y='total_bill',data=tips)

Boxplot
2]sns.boxplot(data=df) #pokemon


2] # Pre-format DataFrame #pokemon

stats_df = df.drop(['Total', 'Stage', 'Legendary'], axis=1)

# New boxplot using stats_df

sns.boxplot(data=stats_df)

2]sns.boxplot(x='day',y='total_bill',data=tips,palette="rainbow")  #palette use for color
#class

2]sns.boxplot(data=tips,palette="rainbow",orient='h')   #class
    #orient ie lkhe ye che ke output y axis pr aave jo orient na lkheye
      na lkheye to output x axis pr aave ie totalbil,tip,size ae x axis pr aave

2] sns.boxplot(x='day',y='total_bill',hue='smoker',data=tips,palette="rainbow")
     #we use hue for comparisn
   #class

Violinplot
#3c ie class ma krelu
3c]sns.violinplot(x='day',y='total_bill',hue='smoker',data=tips,palette="rainbow")

3c]sns.violinplot(x='day',y='total_bill',hue='sex',data=tips,palette="rainbow")



3]#pokemon detaset

# Set theme # sns.set_style('whitegrid') # Violin plot sns.violinplot(x='Type 1', y='Attack', data=df)

Swarm plot pokemon dataset

4]# Swarm plot with Pokemon color palette

sns.swarmplot(x='Type 1', y='Attack', data=df,

              palette=pkmn_type_colors)

4]# Set figure size with matplotlib

plt.figure(figsize=(10,6))

# Create plot

sns.violinplot(x='Type 1',

               y='Attack',

               data=df,

               inner=None, # Remove the bars inside the violins

               palette=pkmn_type_colors)

sns.swarmplot(x='Type 1',

              y='Attack',

              data=df,

              color='k', # Make points black

              alpha=0.7) # and slightly transparent

# Set title with matplotlib

plt.title('Attack by Type')

Swarm plot

5]sns.swarmplot(x='Stat', y='value', data=melted_df,

              hue='Type 1')

5]

# 1. Enlarge the plot plt.figure(figsize=(10,6)) sns.swarmplot(x='Stat',               y='value',               data=melted_df,               hue='Type 1',               split=True, # 2. Separate points by hue               palette=pkmn_type_colors) # 3. Use Pokemon palette # 4. Adjust the y-axis plt.ylim(0, 260) # 5. Place legend to the right plt.legend(bbox_to_anchor=(1, 1), loc=2)

 Heatmap

10.1 - Heatmap

Heatmaps help you visualize matrix-like data.

Heatmap

Python

1 2 3 4 5

# Calculate correlations corr = stats_df.corr() # Heatmap sns.heatmap(corr)

10.2 - Histogram

Histograms allow you to plot the distributions of numeric variables.

Histogram

Python

1 2	# Distribution Plot (a.k.a. Histogram) sns.distplot(df.Attack)

10.3 - Bar Plot

Bar plots help you visualize the distributions of categorical variables.

Bar Plot

Python

1 2 3 4 5

# Count Plot (a.k.a. Bar Plot) sns.countplot(x='Type 1', data=df, palette=pkmn_type_colors) # Rotate x-labels plt.xticks(rotation=-45)

10.4 - Factor Plot

Factor plots make it easy to separate plots by categorical classes.

Factor Plot

Python

1 2 3 4 5 6 7 8 9 10 11 12 13

# Factor Plot g = sns.factorplot(x='Type 1',                    y='Attack',                    data=df,                    hue='Stage',  # Color by stage                    col='Stage',  # Separate by stage                    kind='swarm') # Swarmplot # Rotate x-axis labels g.set_xticklabels(rotation=-45) # Doesn't work because only rotates last plot # plt.xticks(rotation=-45)

10.5 - Density Plot

Density plots display the distribution between two variables.

• Tip: Consider overlaying this with a scatter plot.

Density Plot

Python

1 2	# Density Plot sns.kdeplot(df.Attack, df.Defense)

10.6 - Joint Distribution Plot

Joint distribution plots combine information from scatter plots and histograms to give you detailed information for bi-variate distributions.

Joint Distribution Plot

Python

1 2	# Joint Distribution Plot sns.jointplot(x='Attack', y='Defense', data=df)

GRID

6]

import seaborn as sns

import matplotlib.pyplot as plt

%matplotlib inline

iris=sns.load_dataset('iris')

iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

# pair Grid

7]

sns.PairGrid(iris)

g=sns.PairGrid(iris)

g.map(plt.scatter)