Pandas is one of the most popular Python packages used in data science. Pandas offer a powerful, and flexible data structure ( Dataframe & Series ) to manipulate, and analyze the data. Visualization is the best way to interpret the data.
Python has many popular plotting libraries that make visualization easy. Some of them are matplotlib, seaborn, and plotly. It has great integration with matplotlib. We can plot a dataframe using the plot() method. But we need a dataframe to plot. We can create a dataframe by just passing a dictionary to the DataFrame() method of the pandas library.
Let’s create a simple dataframe:
There are a number of plots available to interpret the data. Each graph is used for a purpose. Some of the plots are BarPlots, ScatterPlots, and Histograms, etc.
To get the scatterplot of a dataframe all we have to do is to just call the plot() method by specifying some parameters.
There are many ways to customize plots this is the basic one.
Similarly, we have to specify some parameters for plot() method to get the bar plot.
The line plot of a single column is not always useful, to get more insights we have to plot multiple columns on the same graph. To do so we have to reuse the axes.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.