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Suraj Jain

2 years ago

- Basic Plotting: plot

- Bar Plot

- Histograms

- Box Plots

- Area Plot

- Scatter Plot

- Pie Chart

In this article, I will try to
take you through some of the basic and most used plots in python pandas.

Plot() method in pandas make plots of DataFrame
and Series using matplotlib / pylab.

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',
periods=10), columns=list('ABCD'))
df.plot()
```

Its the **output **is as follows –

If
the index consists of dates, it calls gct().autofmt_xdate() to format the
x-axis as shown in the above illustration.

We
can plot one column versus another using the x and y keywords.

Plotting
methods allow a handful of plot styles other than the default line plot. These
methods can be provided as the kind keyword argument to plot().

These
include −

- bar or barh for bar plots
- hist for histogram
- box for boxplot
- 'area' for area plots
- 'scatter' for scatter plot
- Pie Chart

A barplot (or
barchart) is one of the most common types of graphic. It shows the relationship
between a numeric and a categoric variable. Each entity of the categoric
variable is represented as a bar. The size of the bar represents its numeric
value.

Let’s visualize it

A bar plot can be created in the following way −

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d')
df.plot.bar()
```

Its
**output **is as follows –

To
produce a stacked bar plot, we have to provide parameter stacked=true −

```
import pandas as pd
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d')
df.plot.bar(stacked=true)
```

Its the **output **is as follows –

Now
if we want to get the horizontal bar plots, we will use the bar method −

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d')
df.plot.barh(stacked=true)
```

Its **output **is as follows –

A histogram is a representation of the distribution of numerical
data, where the data are binned and the count for each bin is represented.

Histograms can be plotted using the plot.hist() method. We can
specify the number of bins.

```
import pandas as pd
import numpy as np
df = pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':
np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
df.plot.hist(bins=20)
```

Its
**output **is as follows –

To plot different histograms for each column, use the following
code −

```
import pandas as pd
import numpy as np
df=pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':
np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
df.diff.hist(bins=20)
```

Its
**output **is as follows –

Boxplot is used to visualize the distribution of values within
each column.

It can be drawn calling
Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() .

So, let’s visualize, here is a boxplot representing five trials
of 10 observations of a uniform random variable on [0,1).

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot.box()
```

Its
**output **is as follows –

Area plot can be created using the Series.plot.area() or the
DataFrame.plot.area() methods.

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area()
```

Its
**output **is as follows –

Scatter the plot can be plot using the DataFrame.plot.scatter() methods.

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')
```

Its the **output **is as follows –

Pie the chart can be plot using the DataFrame.plot.pie() method.

```
import pandas as pd
import numpy as np
df = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])
df.plot.pie(subplots=true)
```

Its **output **is
as follows −

I hope you enjoyed reading this article and finally, you came
to know about **Visualization with Python Pandas.**

For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.

Thanks for reading…

Happy Learning…