pandas Visualization: easy to use in the various figures

First, the name of several Matplotlib map

1. Line chart: plot
2. Column charts: bar
3. Histogram: hist
4. Boxplot: box
5. Density map: kde
6. Area Chart: area
7. Scatter: scatter
8. Scatterplot matrix: scatter_matrix
9. Pie: pie

 

Second, the line chart: plot

　　Average need to sort after the showing in FIG.

　　df.avg.value_counts().sort_index().plot()

 

Third, the column chart: bar

　　You can do first pivot , and then generates a bar graph

　　df.pivot_table(index='city',columns='education',values='avg',aggfunc='count').plot.bar()

 

　　If this is done stacked column chart , you can set the bar () parameters

　　df.pivot_table(index='city',columns='education',values='avg',aggfunc='count').plot.bar(stacked=True)　

　　

　　If you want to make a bar graph, you can modify the bar () method bar ()

　　df.pivot_table(index='city',columns='education',values='avg',aggfunc='count').plot.barh()

 

Fourth, the histogram: hist

　　df.avg.plot.hist()

　　To "education" field multi-dimensional analysis, the average draw a histogram,

　　alpha: transparency pattern;

　　stacked: whether the stack;

　　bins: Density;

　　df.groupby('education').apply(lambda x:x.avg).unstack().T.plot.hist(alpha=0.5,stacked=True,bins=30)

 

V. boxplot: box

　　Use one: the "Histogram" similar

　　df.groupby('education').apply(lambda x:x.avg).unstack().T.plot.box()

　　Usage of Two:

　　df.boxplot(column='avg',by='education')

 

Sixth, the density map: kde

　　df.avg.plot. wherein ()

 

Seven area chart: area

　　Usually the data classification (pivot) ,

　　df.pivot_table(index='avg',columns='education',values='positonId',aggfunc='count').plot.area()

 

 Eight, scatter plots: scatter

　　Classified by company, and the average x-axis, y-axis is the number of

　　df.groupby('companyId').aggregate(['mean','count']).avg.plot.scatter(x='mean',y='count')

 

Nine, scatter plot matrix: scatter_matrix (Pandas function)

　　It applies to two or more parameters, combined two by two

　　matrix=df.groupby('companyId').aggregate(['mean','count',max]).avg

　　pd.plotting.scatter_matrix(matrix.query('count<50'),diagonal='kde')

　　Query: count is less than 50

　　diagonal: FIG modified type of (kde: FIG density)

　　

Ten, pie charts: pie

　　df.city.value_counts().plot.pie(figsize=(6,6))

　　Length and width of the FIG: figsize