Three cool dynamic interactive charts are selected, which are generated by pandas with one click and are easy to understand

Hello everyone ！

Today, let's talk about how to use one line of code in DataFrame Generate cool dynamic interactive charts in the dataset , Let's first introduce the modules we need to use this time cufflinks

Module installation

Involving installation , direct pip install that will do

pip install cufflinks

The import module , And view the relevant configuration

We import the module , Let's see what the current version is

cf.__version__

output

'0.17.3'

At present, the version of the module has reached 0.17.3, It's also the latest version , Then, what charts can be drawn in our latest version

cf.help()

output

Use 'cufflinks.help(figure)' to see the list of available parameters for the given figure.
Use 'DataFrame.iplot(kind=figure)' to plot the respective figure
Figures:
 bar
 box
 bubble
 bubble3d
 candle
 choroplet
 distplot
 .......

From the output above we can see , The general syntax for drawing a chart is df.iplot(kind= Chart name ) How do we want to view the parameters when drawing a specific chart , For example, a histogram bar What are the parameters , You can do that

cf.help('bar')

Histogram

Let's first look at the drawing of histogram chart , First, let's create a data set for chart drawing

df2 = pd.DataFrame({'Category':['A','B','C','D'],
                    'Values':[95,56,70,85]})
df2

output

  Category  Values
0        A      95
1        B      56
2        C      70
3        D      85

Then let's draw the histogram

df2.iplot(kind='bar',x='Category',y='Values',
          xTitle = "Category",yTitle = "Values",
          title = " Histogram ")

output

Among them x The parameter is filled with x The corresponding variable name on the axis , and y The parameter is filled in y The corresponding variable name on the axis , We can draw the chart in png Download it in the format of ,

At the same time, we can also zoom in on the chart ,

Let's take a look at the following set of data

df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split())
df.head()

output

          A         B         C         D
0  0.612403 -0.029236 -0.595502  0.027722
1  1.167609  1.528045 -0.498168 -0.221060
2 -1.338883 -0.732692  0.935410  0.338740
3  1.662209  0.269750 -1.026117 -0.858472
4  1.387077 -0.839192 -0.562382 -0.989672

Let's plot the histogram

df.head(10).iplot('bar')

output

We can also draw “ Stacked ” Histogram

df.head(10).iplot(kind='bar',barmode='stack')

output

So again , We can also draw the histogram horizontally

df.head(10).iplot(kind='barh',barmode='stack')

output

Broken line diagram

Now let's take a look at the drawing of line chart , Let's start with the above df The columns of the dataset are accumulated

df3 = df.cumsum()

Then let's draw a line chart

df3.iplot()

output

Of course, you can also filter out a few columns and draw them , The effect is as follows

df3[["A", "B"]].iplot()

output

We can also draw a straight line to fit its trend ,

df3['A'].iplot(bestfit = True,bestfit_colors=['pink'])

output

Here we will focus on introducing a iplot() Parameters commonly used in methods

• kind: Chart type , The default is scatter, Scatter type , There are other types to choose from bar( Histogram )、box( Box figure )、heatmap( Heat map ) wait
• theme: Layout theme , Can pass cf.getThemes() To see what are the main
• title: The title of the chart
• xTitle/yTitle: x perhaps y The name of the shaft above the shaft
• colors: The color when drawing the chart
• subplots: Boolean value , When drawing subgraphs, you need , The default is False
• mode:  character string , Drawing mode , There can be lines、markers, There's also lines+markers and lines+text Equal mode
• size: For scatter charts , It is mainly used to adjust the size of scatter points
• shape: When drawing subgraphs, the layout of each graph
• bargap: The distance between columns in the histogram
• barmode : The shape of histogram ,stack( Stacked )、group( Clusters )、overlay( Cover )

Area map

The transition from line graph to area graph is very simple , Only the parameters fill Set to True that will do , The code is as follows

df3.iplot(fill = True)

output