Let us see how to create a DataFrame from a Numpy array. We will also learn how to specify the index and the column headers of the DataFrame.
Approach :
- Import the
Pandas
andNumpy
modules. - Create a
Numpy
array. - Create list of index values and column values for the DataFrame.
- Create the DataFrame.
- Display the DataFrame.
Example 1 :
# importiong the modules import pandas as pd
import numpy as np
# creating the Numpy array array = np.array([[ 1 , 1 , 1 ], [ 2 , 4 , 8 ], [ 3 , 9 , 27 ],
[ 4 , 16 , 64 ], [ 5 , 25 , 125 ], [ 6 , 36 , 216 ],
[ 7 , 49 , 343 ]])
# creating a list of index names index_values = [ 'first' , 'second' , 'third' ,
'fourth' , 'fifth' , 'sixth' , 'seventh' ]
# creating a list of column names column_values = [ 'number' , 'squares' , 'cubes' ]
# creating the dataframe df = pd.DataFrame(data = array,
index = index_values,
columns = column_values)
# displaying the dataframe print (df)
|
Output :
Example 2 :
# importiong the modules import pandas as pd
import numpy as np
# creating the Numpy array array = np.array([[ 'Aditya' , 20 ], [ 'Samruddhi' , 15 ],
[ 'Rohan' , 21 ], [ 'Anantha' , 20 ],
[ 'Abhinandan' , 21 ]])
# creating a list of index names index_values = [ 'A' , 'B' , 'C' , 'D' , 'E' ]
# creating a list of column names column_values = [ 'Names' , 'Age' ]
# creating the dataframe df = pd.DataFrame(data = array,
index = index_values,
columns = column_values)
# displaying the dataframe print (df)
|
Output :
Example 3 :
# importiong the modules import pandas as pd
import numpy as np
# creating the Numpy array array = np.array([[ 'CEO' , 20 , 5 ], [ 'CTO' , 22 , 4.5 ],
[ 'CFO' , 21 , 3 ], [ 'CMO' , 24 , 2 ]])
# creating a list of index names index_values = [ 1 , 2 , 3 , 4 ]
# creating a list of column names column_values = [ 'Names' , 'Age' ,
'Net worth in Millions' ]
# creating the dataframe df = pd.DataFrame(data = array,
index = index_values,
columns = column_values)
# displaying the dataframe print (df)
|
Output :