Various image using pandas and matplotlib



Use these commands to install matplotlib, pandas, and numpy:

 pip install matplotlib pip install pandas pip install numpy 

Parcel types:

  1. Basic plotting. In this basic plotting, we can use randomly generated data to plot using series and matplotlib.

    # import libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    ts = pd.Series (np.random.randn ( 1000 ), in dex = pd.date_range (

    `1/1 / 2000` , periods = 1000 ))

    ts = ts.cumsum ()

    ts.plot ()

     
    plt.show ()

    Output:

  2. Different data plot: using more than one list of data in a plot.

    # importing libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

      

    ts = pd.Series ( np.random.randn ( 1000 ), index = pd.date_range (

    `1/1 / 2000` , periods = 1000 ))

     

    df = pd.DataFrame (np.random.randn ( 1000 , 4 ), 

    index = ts.index, columns = list ( `ABCD` ))

     

    df = df.cumsum ()

    plt.figure ()
    df .plot ()
    plt.show ()

    Output:

    , Li & gt;

  3. Plot on a given axis: we can explicitly define an axis name and plot data based on that axis.

    # importing libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    ts = pd.Series (np .random.randn ( 1000 ), index = pd.date_range (

    `1/1/2000 ` , periods = 1000 ))

      

    df = pd.DataFrame (np .random.randn ( 1000 , 4 ), index = ts.index,

    columns = list ( `ABCD` ))

     

    df3 = pd.DataFrame (np.random.randn ( 1000 , 2 ),

      columns = [ `B` , ` C` ]). cumsu m ()

     

    df3 [ `A` ] = pd.Series ( list ( range ( len (df))))

    df3.plot (x = ` A` , y = `B` )

    plt.show ()

    Output:

  4. Histogram using matplotlib: Look for different types of histogram to clearly understand the behavior of the data.

    # importing libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    ts = pd.Series (np.random.randn ( 1000 ), index = pd.date_range (

    `1/1 / 2000` , periods = 1000 ))

     

    df = pd.DataFrame (np.random.randn ( 1000 , 4 ), index = ts.index,

    columns = list ( ` ABCD` ))

     

    df3 = pd.DataFrame (np.random.randn ( 1000 , 2 ),

    columns = [ `B` , ` C` ]). cumsum ()

     

    df3 [ `A` ] = pd.Series ( list ( range ( len (df))))

    df3.iloc [ 5 ]. plot.bar ()

    plt.axhline ( 0 , color = `k ` )

      
    plt.show ()

    Output:

  5. Histograms:

    # import libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

      

    df4 = pd.DataFrame ({ `a` : np.random.randn ( 1000 ) + 1

    `b` : np.random.randn ( 1000 ), 

    `c` : np.random.randn ( 1000 ) - 1 },

    columns = [ ` a` , `b` , `c` ])

    plt.figure ()

     

    df4.plot.hist (alpha = 0.5 )

    plt.show ()

    Exit:

  6. Plot using Series and matplotlib: use a block to plot the data.

    # importing libraries

    import matplotlib.pyplot as plt

    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 ()
    plt.show ()

    Output:

  7. Plot density:

    # importing libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    df = pd.DataFrame (np.random.rand ( 10 , 5 ), 

    columns = [ `A` , ` B` , `C` , `D` , ` E` ])

     

    ser = pd.Series (np.random.randn ( 1000 ))

    ser.plot.kde ()

     
    plt.show ()

    Output:

  8. Plot area using matplotlib:

    # library import

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    df = pd.DataFrame (np.random.ran d ( 10 , 5 ), 

      columns = [ `A` , `B` , ` C` , `D` , ` E` ])

      
    df.plot.area ()
    plt.show ()

    Output:

  9. Dot plot:

    # importing libraries

    import matplotlib .pyplot as plt

    import pandas as pd

    import numpy as np

     

    df = pd.DataFrame (np.random.rand ( 500 , 4 ),

    columns = [ `a` , `b` , ` c` , `d` ])

     

    df.plot.scatter (x = `a` , y = ` b` )

    plt.show ()

    Output:

  10. Hexagonal bunker

    # importing libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    df = pd.DataFrame (np.random.randn ( 1000 , 2 ) , columns = [ `a` , `b` ])

     

    df [ `a` ] = df [ ` a` ] + np.arange ( 1000 )

    df.plot.hexbin (x = ` a` , y = `b` , gridsize = 25 )

    plt.show ()

    Output:

  11. Circular plot:

    # import libraries

    import matplotlib.pyplot as plt

    import pandas as pd

    import numpy as np

     

    series = pd.Series ( 3 * np.random.rand ( 4 ) ,

    index = [ `a` , `b` , ` c` , `d` ], name = ` series` )

     

    series.plot.pie (figsize = ( 4 , 4 ))

    plt.show ()

    Output: