Python data visualization Seaborn (IV) -- classification data visualization

The previous article focused on both variables Numerical variable The situation of , When a variable is a categorical variable , We need other types of graphs to show the analysis data . stay seaborn There are many types of graphics in and they are very easy to use .

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(,font_scale=1.4,context="paper")
# Set style 、 scale 
import warnings
warnings.filterwarnings('ignore')
# No warning 

seaborn in , The classification diagram is mainly divided into three parts ：

• Classified scatter plot ：
  • stripplot( Default ,kind = “strip”)
  • swarmplot(kind = “swarm”)
• Classification map ：
  • boxplot(kind=“box”)
  • violinplot(kind=“violin”)
  • boxenplot(kind=“boxen”)
• Classification estimation diagram ：
  • pointplot(kind=“point”)
  • barplot(kind=“bar”)
  • countplot(kind=“count”)

The above three series represent data of different granularity levels . Of course , In the course of practical use , There is no need to remember so much , because seaborn The classification series in has a unified graphical interface catplot(), Just this one function , Access to all classified image types .

Classified scatter plot

seaborn.stripplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=‘gray’, linewidth=0, ax=None, **kwargs)

• jitter : Whether it shakes ,True,false or float
• dodge : When there is hue When parameters are , Whether different colors are separated along the axis
• orient : Graphic orientation , vertical （“v”） Or level (“h”)

# 1、catplot() By default ,kind='strip'
# Plot the distribution scatter diagram of the sample data according to different categories 
tips = sns.load_dataset("tips")
print(tips.head())
# Load data 
sns.catplot(x="day", # x → Set the grouping statistics field 
y="total_bill", # y → Data distribution statistics field 
# here xy Data exchange , This will make the scatter plot horizontally distributed 
data=tips, # data → Corresponding data 
jitter = True, height=6,
# When the point data overlaps more ,jitter You can control point jitter , You can also set the spacing as ：jitter = 0.1
s = 6, edgecolor = 'w',linewidth=1,marker = 'o' ,
# Set point size 、 Stroke color or width 、 Point style 
)

 total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

# 1、stripplot()
# adopt kind='swarm' To adjust the points to prevent coincidence 
sns.catplot(x="day", y="total_bill",kind='swarm',
hue='sex',data=tips,height=5,s=5.5)
# Prevent coincidence by distributing points along the axis , This only works with smaller datasets 

# 1、stripplot()
# Set the color palette 
sns.catplot(x="sex", y="total_bill", hue="day",
data=tips, jitter=True,
palette="Set2", # Set the color palette 
dodge=True, # Whether to split 
)

# Sort 
print(tips['day'].value_counts())
# see day The unique value of the field 
sns.catplot(x="day", y="total_bill", data=tips,
order = ['Sun','Sat'])
# order → Filter categories , Control sorting 

Sat 87
Sun 76
Thur 62
Fri 19
Name: day, dtype: int64

Classification map

boxplot boxplot()

seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None, **kwargs)

• saturation : float, Color saturation
• fliersize : The size of the outlier marker
• whis : float, beyond IQR How many proportions are considered outliers , Default 1.5
• notch : Whether to set the groove with the median

# boxplot catplot(kind='box')
sns.catplot(x='day', y='total_bill', data=tips,
kind='box',linewidth=2, # Line width 
width=0.6, # The space ratio between boxes 
fliersize=5, # Outlier size 
palette='hls', # palette 
whis=1.5, # Set up IQR
notch=True, # Set whether to make grooves with median 
order=['Thur', 'Fri', 'Sat', 'Sun'], # Filter categories 
)

# adopt hue Parameter reclassification 
# Multiple types of graphs are mixed 
# Draw a box diagram 
sns.catplot(x="day", y="total_bill", data=tips,
kind='box',hue = 'smoker',height=6)
# Draw a scatter plot 
sns.swarmplot(x="day", y="total_bill", data=tips,
color ='k',s= 3,alpha = 0.8)
# Add classified scatter chart , To add a scatter plot here, use the respective functions swarmplot()
# No more advanced ports catplot() Otherwise, there are two figures 

For datasets with a large amount of data , The scatter chart will be very crowded , Now we can use boxenplot(), This kind of chart is similar to the box chart , It can not only display the distribution of data, but also display the statistical information of data as a box diagram

diamonds = sns.load_dataset("diamonds")
print(diamonds.head(3))
sns.catplot(x='color',y='price',kind='boxen',
data=diamonds.sort_values("color"),
height=6)

 carat cut color clarity depth table price x y z
0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31

Picture of violin

Violin diagram combines kernel density estimation with box diagram

seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw=‘scott’, cut=2, scale=‘area’, scale_hue=True, gridsize=100, width=0.8, inner=‘box’, split=False, dodge=True, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)

• bw : (“scott”,“silverman”,float), Nuclear size scale factor , The actual effect is that the larger the more smooth .
• cut : float, Used to extend density beyond extreme data points , Set to 0 To limit the violin range to the range of observation data .
• scale : The width of the violin diagram ：area- The same area ,count- Determine the width according to the number of samples ,width- The width is the same
• scale_hue : bool, When there is hue when , Decide whether all Violins in the real group or on the graph are scaled
• gridsize : And must read to estimate the number of points in the discrete grid , The higher, the smoother
• inner : （“box”, “quartile”, “point”, “stick”, None）, Internal display style
• split : When there are nested colors , Whether to draw the violin on each side separately .

# 2、violinplot()
# Violin chart 
sns.catplot(x="day", y="total_bill", data=tips,
kind='violin',linewidth = 2, # Line width 
width = 0.8, # The space ratio between boxes 
height=6,palette = 'hls', # Set up the palette 
order = ['Thur','Fri','Sat','Sun'], # Filter categories 
scale = 'area',
# Measure the width of a violin graph ：
# area- The same area ,count- Determine the width according to the number of samples ,width- The width is the same 
gridsize = 30, # Set the smoothness of the violin graph edges , The higher, the smoother 
inner = 'box',
bw = .5 # Control the degree of fit , Generally, it is not necessary to set 
)

# 2、violinplot()
# adopt hue Parameter reclassification 
sns.catplot(x="day", y="total_bill", data=tips,
kind='violin',hue = 'smoker',
palette="muted", split=True, # Set whether to split the violin diagram 
inner="quartile",height=6)

# 2、violinplot()
# Combined with scatter diagram 
sns.catplot(x="day", y="total_bill", data=tips,
kind='violin',palette = 'hls',
inner = None,height=6,
cut=0 # Set to 0, Limit the graph to the observed data .
)
# Insert scatter plot 
sns.swarmplot(x="day", y="total_bill", data=tips,
color="k", alpha=.5)

The statistical figure

seaborn.barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor=’.26’, errwidth=None, capsize=None, dodge=True, ax=None, **kwargs)

• estimator : Statistical functions used in the sorting box
• ci : （float,“sd”,None）
• units : Variable name , Each sample of a variable is plotted separately , Can be used to plot duplicate data
• errwidth : Error bar width
• capsize : Width of error bar cap

# 1、barplot()
# confidence interval ： Sample mean + Sampling error 
titanic = sns.load_dataset("titanic")
# print(titanic.head())
# Load data 
sns.catplot(x="sex", y="survived", data=titanic,
kind='bar',palette = 'hls', hue="class",
order = ['male','female'], # Filter categories 
capsize = 0.05, # Horizontal extension width of error line 
saturation=.8, # Color saturation 
errcolor = 'gray',errwidth = 2, # Error bar color , Width 
height=6,ci = 'sd'
# Confidence interval error → 0-100 Internal value 、'sd'、None
)
print(titanic.groupby(['sex','class']).mean()['survived'])
print(titanic.groupby(['sex','class']).std()['survived'])
# Calculate the data 

sex class
female First 0.968085
Second 0.921053
Third 0.500000
male First 0.368852
Second 0.157407
Third 0.135447
Name: survived, dtype: float64
sex class
female First 0.176716
Second 0.271448
Third 0.501745
male First 0.484484
Second 0.365882
Third 0.342694
Name: survived, dtype: float64

# 1、barplot()
# Histogram - Confidence interval estimate 
# You can change your style like this 
sns.catplot(x="day", y="total_bill", data=tips,
linewidth=2.5,facecolor=(1,1,1,0),
kind='bar',edgecolor = 'k',)

# 1、barplot()
crashes = sns.load_dataset("car_crashes").sort_values("total", ascending=False)
print(crashes.head())
# Load data 
f, ax = plt.subplots(figsize=(10, 15))
# Create a chart 
# sns.set_color_codes("pastel")
sns.barplot(x="total", y="abbrev", data=crashes,
label="Total", color="b",edgecolor = 'w')
# Set the first histogram 
# sns.set_color_codes("muted")
sns.barplot(x="alcohol", y="abbrev", data=crashes,
label="Alcohol-involved", color="y",edgecolor = 'w')
# Set the second histogram 
ax.legend(ncol=2, loc="lower right")
sns.despine(left=True, bottom=True)

 total speeding alcohol not_distracted no_previous ins_premium \
40 23.9 9.082 9.799 22.944 19.359 858.97
34 23.9 5.497 10.038 23.661 20.554 688.75
48 23.8 8.092 6.664 23.086 20.706 992.61
3 22.4 4.032 5.824 21.056 21.280 827.34
17 21.4 4.066 4.922 16.692 16.264 872.51
ins_losses abbrev
40 116.29 SC
34 109.72 ND
48 152.56 WV
3 142.39 AR
17 137.13 KY

# 2、countplot()
# Counting histogram 
sns.catplot(x="class", hue="who", data=titanic,
kind='count',palette = 'magma')
sns.catplot(y="class", hue="who", data=titanic,
kind='count',palette = 'magma')
# x/y → With x perhaps y Axis drawing （ The transverse , vertical ）
# Usage and barplot be similar 

# 3、pointplot()
sns.catplot(x="time", y="total_bill", hue = 'smoker',data=tips,
kind='point',palette = 'hls',height=7,
dodge = True, # Whether the set points are separated 
join = True, # Whether to connect 
markers=["o", "x"], linestyles=["-", "--"], # Set point style 、 Linetype 
)
# Calculate the data 
# # Usage and barplot be similar