Data-Visualization-Using-Pandas

Data visualization is an essential step in making data science projects successful — an effective plot tells a thousand words.

Data visualization is a powerful way to capture trends and share the insights gained from data. There are plenty of data visualization tools on the shelf with a lot of outstanding features, but in this tutorial, we're going to learn plotting with the Pandas package.

We're going to work on the weekly closing price of the Facebook, Microsoft, and Apple stocks over the last previous months

Features

• Line Plot
• Bar Plot
• Histogram
• Box Plot
• Area Plot
• Pie Plot
• Scatter Plot
• Hexbin Plot
• KDE Plot

Programing Language

• Python

Libraries

• Pandas
• Numpy
• Matplotlib

Line Plot

The default plot is the line plot that plots the index on the x-axis and the other numeric columns in the DataFrame on the y-axis.

df.plot(y='MSFT', figsize=(9,6))

We can plot multiple lines from the data by providing a list of column names and assigning it to the y-axis.

df.plot.line(y=['FB', 'AAPL', 'MSFT'], figsize=(10,6))

We can use the other parameters provided by the plot() method to add more details to a plot, like this:

df.plot(y='FB', figsize=(10,6), title='Facebook Stock', ylabel='USD')

As we see in the figure, the title argument adds a title to the plot, and the ylabel sets a label for the y-axis of the plot. The plot's legend display by default, however, we may set the legend argument to false to hide the legend

Bar Plot

A bar chart is a basic visualization for comparing values between data groups and representing categorical data with rectangular bars. This plot may include the count of a specific category or any defined value, and the lengths of the bars correspond to the values they represent.

We'll create a bar chart based on the average monthly stock price to compare the average stock price of each company to others in a particular month. To do so, first, we need to resample data by month-end and then use the mean() method to calculate the average stock price in each month. We also select the last three months of data, like this:

df_3Months = df.resample(rule='M').mean()[-3:]
print(df_3Months)

Now, we're ready to create a bar chart based on the aggregated data by assigning the bar string value to the kind argument:

df_3Months.plot(kind='bar', figsize=(10,6), ylabel='Price')

We can create horizontal bar charts by assigning the barh string value to the kind argument.

df_3Months.plot(kind='barh', figsize=(9,6))

To create a stacked bar chart we need to assign True to the stacked argument, like this:

df_3Months.plot(kind='bar', stacked=True, figsize=(9,6))

Histogram

A histogram is a type of bar chart that represents the distribution of numerical data where the x-axis represents the bin ranges while the y-axis represents the data frequency within a certain interval.

df[['MSFT', 'FB']].plot(kind='hist', bins=25, alpha=0.6, figsize=(9,6))

A histogram can also be stacked. Let's try it out:

df[['MSFT', 'FB']].plot(kind='hist', bins=25, alpha=0.6, stacked=True, figsize=(9,6))

Box Plot

A box plot consists of three quartiles and two whiskers that summarize the data in a set of indicators: minimum, first quartile, median, third quartile, and maximum values. A box plot conveys useful information, such as the interquartile range (IQR), the median, and the outliers of each data group.

df.plot(kind='box', figsize=(9,6))

We can create horizontal box plots, like horizontal bar charts, by assigning False to the vert argument

df.plot(kind='box', vert=False, figsize=(9,6))

Area Plot

An area plot is an extension of a line chart that fills the region between the line chart and the x-axis with a color. If more than one area chart displays in the same plot, different colors distinguish different area charts.

df.plot(kind='area', figsize=(9,6))

Creates a stacked area plot by default:

df.plot(kind='area', stacked=False, figsize=(9,6))

Pie Plot

A pie plot is a great proportional representation of numerical data in a column.

df_3Months.index=['March', 'April', 'May']
df_3Months.plot(kind='pie', y='AAPL', legend=False, autopct='%.f')

Represent the data of all the columns in multiple pie charts as subplots

Scatter Plot

Scatter plots plot data points on the x and y axes to show the correlation between two variables.

df.plot(kind='scatter', x='MSFT', y='AAPL', figsize=(9,6), color='Green')

Hexbin Plot

When the data is very dense, a hexagon bin plot, also known as a hexbin plot, can be an alternative to a scatter plot. In other words, when the number of data points is enormous, and each data point can't be plotted separately, it's better to use this kind of plot that represents data in the form of a honeycomb. Also, the color of each hexbin defines the density of data points in that range.

df.plot(kind='hexbin', x='MSFT', y='AAPL', gridsize=10, figsize=(10,6))

KDE Plot

Kernel Density Estimate, also known as KDE,visualizes the probability density of a continuous and non-parametric data variable. This plot uses Gaussian kernels to estimate the probability density function (PDF) internally.

df.plot(kind='kde')

We're can also specify the bandwidth that affects the plot smoothness in the KDE plot

df.plot(kind='kde', bw_method=0.1)

df.plot(kind='kde', bw_method=1)

As we can see, selecting a small bandwidth leads to under-smoothing, which means the density plot appears as a combination of individual peaks. On the contrary, a huge bandwidth leads to over-smoothing, which means the density plot appears as unimodal distribution.