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Pandas uses another popular Python module called Matplotlib for visualization. Usually, visualization is done from data that is stored as pandas objects. This chapter covers a few simple examples showing visualizing data using pandas.

## Plot a **Line Plot** using Pandas

A line plot of data can be made using plot() function of DataFrame object as shown in the example below:

# Making necessary imports import pandas as pd import matplotlib.pyplot as plt # Load data into dataframe data = pd.read_csv("https://github.com/plotly/datasets/raw/master/2014_apple_stock.csv", index_col=0) # Plot the line from dataframe specifying x and y labels data.plot() plt.xlabel('time') plt.ylabel('value')

**Plot a Scatter Plot using Pandas**

A scatter plot of data can be made using plot.scatter() function of DataFrame object as shown in the example below:

# Making necessary imports import pandas as pd # Loading the data into dataframe df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], [6.4, 3.2, 1], [5.9, 3.0, 2]], columns=['length', 'width', 'species']) # Making the scatter plot ax1 = df.plot.scatter(x='length', y='width', c='DarkBlue')

**Plot a Histogram Plot using Pandas**

A histogram plot of data can be made using plot.hist() function of DataFrame object as shown in the example below:

''' This histogram below shows the distribution of each value when we: - draw one dice 9000 times - draw two dices 9000 time and sum the result ''' # Making necessary imports import pandas as pd import numpy as np # Create dataframe df = pd.DataFrame( np.random.randint(1, 7, 9000), columns = ['one']) # Add new column to dataframe by using addition df['two'] = df['one'] + np.random.randint(1, 7, 9000) ax = df.plot.hist(bins=12, alpha=0.8)

**Plot a Bar Graph using Pandas**

A bar plot of data can be made using pandas.DataFrame.plot.bar() function of DataFrame object as shown in the example below:

# Making necessary imports import pandas as pd import numpy as np # Loading the data into dataframe df = pd.DataFrame({'Cake_items':['kitkat', 'Unicorn', 'Chocolateroll', 'Barbiedoll','Doraemon'], 'Sales':[235,554,582,695,545]}) # Making the bar plot ax = df.plot.bar(x='Cake_items', y='Sales', rot=0)

**Saving plots in Pandas**

Pandas plots can be easily saved using savfig() function of matplotlib as shown in the example below:

# plot made using pandas ax = df.plot() # getting the figure from plot fig = ax.get_figure() # saving plot as png image fig.savefig('plot.png') # This saves to current directory; you can also specify some other paths

With this, we have come to the end of our Pandas for Data Science Course.

We hope that this course helped you as a stepping stone towards your Data Science journey. Also, if you have any questions or feedback, please feel free to let us know in the comment section. Feel free to browse for other courses on our platform to continue your journey towards Data Science with Python.

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