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This article introduces the knowledge of "how to use the seaborn package in the python project". Many people will encounter this dilemma in the operation of the actual case, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

The Seaborn package is an enhanced version of mapplotlib and can only be used after installing mapplotlib.

All graphics are displayed by plt.show (), or you can create a canvas using the following method

Picture, ax=PLT. Submissions () # a canvas

Figure, (ax1,ax2) = PLT. Regional plot (ncols=2) # 2 canvas 1) single feature statistical graph countplot

The Sn.countplot (train.mnth) # discrete feature can be used to describe the number of occurrences of sample points.

2) single feature statistical graph distplot

Sn . Distplot (train. Cnt . Values, bin=50,kde=True) # can use continuous features, bin=50 is divided into 50 columns, and kde=True displays nuclear density lines. If the tail of the graph is special and may be a singularity (outlier, noise point), consider removing it.

3) double-characteristic violin picture

SN . Violin Plot (data=train ['yr', 'CNT']], Xerox' yr', yearly 'CNT') # shows the data distribution and its probability density The thick black bar in the middle represents the quartile range, the thin black line extending from it represents the 95% confidence interval, and the white point is the median.

4) double characteristic box diagram

Sn . Box chart (data=train,x=' yr', box 'CNT') #, also known as box chart, box chart or box chart, is a statistical chart used to display a set of discrete information of data. It displays outliers, maximum, upper quartile, median, lower quartile, minimum, and outlier from top to bottom (sometimes none, if any, pay special attention).

5) double feature bar graph

Figure, (ax1,ax2) = PLT. Regional plot (ncols=2) # one canvas, two axes

Sn . Barplot (data=train,x=' holiday', yearly 'CNT', hue=' weathersit', ax=ax1) # hue=' weathersit', distinguished by weathersit

Sn . Barplot (data=train,x=' working day', yearly 'CNT', hue=' season', ax=ax2) # hue=' season', distinguished by season

6) double characteristic line chart

Figure, ax=plt. Feeder plot ()

Sn. Point plot (data=train ['dayofyear', 'cnt', 'yr'], x='dayofyear', y='cnt', hue='yr', ax=ax)

# hue='yr 'refers to the year of honor. Hue refers to color.

Ax. Set (title=' dayly distribution of counts')

PLT. Show ()

7) Relational thermal map

CorrMatt=train [['temp', 'atemp', 'hum', 'windspeed', 'cnt']. Corr ()

Mask=np.array (corrMatt)

Mask [NP. Tril _ indexs _ from (mask)] = False

Sn.heatmap (corrMatt,mask=mask

Vmax=1, squared = true, cannot = true)

PLT. Show ()

8) double characteristic scatter plot

Sn. Scatter plot (x=train). GrLivArea,y=train . SalePrice)

PLT. Title ('looking for outliers') # graphic title

PLT. Show ()

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