Data Viz Crash Course



In [1]:

    
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

For Jupyter Notebooks Only



In [2]:

    
%matplotlib inline

For other IDES use plt.show() after your plot commands



In [3]:

    
x = np.arange(0, 10)



In [4]:

    
y = x ** 2



In [5]:

    
x









    Out[5]:





array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])



In [6]:

    
y









    Out[6]:





array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81], dtype=int32)



In [7]:

    
plt.plot(x, y)









    Out[7]:





[<matplotlib.lines.Line2D at 0x128bea031d0>]



In [8]:

    
# Visualizing the data with a specific marker
plt.plot(x, y, marker = '*')









    Out[8]:





[<matplotlib.lines.Line2D at 0x128beae77f0>]



In [9]:

    
# Visualizing the data with a marker and linestyle
plt.plot(x, y, 'r--')









    Out[9]:





[<matplotlib.lines.Line2D at 0x128beb84a90>]



In [10]:

    
# Visualizing the data with a marker and linestyle (with arguments description)
plt.plot(x, y, color = 'r', linestyle = '--')









    Out[10]:





[<matplotlib.lines.Line2D at 0x128bec223c8>]



In [11]:

    
# Zooming, axis naming and title
plt.plot(x, y, 'r--')
plt.xlim(2, 4)
plt.ylim(10, 20)
plt.title("Zoomed")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")









    Out[11]:





<matplotlib.text.Text at 0x128beb43128>



In [12]:

    
mat = np.arange(0, 100).reshape(10, 10)



In [13]:

    
# Show image from an array
plt.imshow(mat)









    Out[13]:





<matplotlib.image.AxesImage at 0x128bed50eb8>



In [14]:

    
mat = np.random.randint(0, 100, (10, 10))



In [15]:

    
plt.imshow(mat)









    Out[15]:





<matplotlib.image.AxesImage at 0x128bedf1da0>



In [16]:

    
# Changing color map
plt.imshow(mat, cmap = 'coolwarm')
plt.colorbar()









    Out[16]:





<matplotlib.colorbar.Colorbar at 0x128bee2d240>

Pandas Plotting



In [17]:

    
df = pd.read_csv('salaries.csv')



In [18]:

    
df



In [19]:

    
# Scatter plot
df.plot(x = 'Salary',
        y = 'Age',
        kind = 'scatter')









    Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x128beef1b38>



In [20]:

    
# Histogram
df.plot(x = 'Salary',
        kind = 'hist')









    Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x128bf00ac50>

	Name	Salary	Age
0	John	50000	34
1	Sally	120000	45
2	Alyssa	80000	27