• Define Dataframes.
• What are the characteristics of Dataframes?
• Which packages are needed to be imported to create dataframes? Explain with syntax and example.
• How to create and display an empty dataframe?
• Create a dataframe with these data and display with single column: [33,55,66,39,45]

• Create a dataframe with the above data (Q-5) and display the sum of the given numbers:

  import pandas as pd
  def df_SingleColumn():
    l = [33,55,66,39,45]
    sum1=0
    df = pd.DataFrame(l)
    sum1=df.sum()
    print("Sum of given numbers:", sum1.to_string(index=False))
  df_SingleColumn()
  

• What will be the output of following code:

import pandas as pd
def df_data():
   df1=pd.DataFrame([22,56,78])
   df2=pd.DataFrame([[11,43,67]])
   print(df1)
   print(df2)
df_data()

Answer:

    0
0  22
1  56
2  78
    0   1   2
0  11  43  67 

In this code two datafames created. In the first dataframe there is a single-column list is which creates a single column. Similarly in the second dataframe, multiple columns created using double square brackets.

Watch the complete video lesson

• Create a dataframe named booking with the following data:

TCode	Name	Tickets	Amount
T0001	Anuj Maheta	5	1355
T0002	Sandeep Oza	2	1169
T0003	Manvi Sharma	6	1988

import pandas as pd
def df_booking():
    data = [['T0001','Anuj Maheta',5,1355],
            ['T0002','Sandeep Oza',2,1169,],
            ['T0003','Manavi Sharma',6,1988]]
    booking=pd.DataFrame(data,columns=['Tcode','Name','Tickets','Amount'])
    print(booking.to_string(index=False))
df_booking()

• Create a dataframe furniture shown in the following table:

Item	Material	Colour	Price
Sofa	Wooden	Maroon	25000
Dining Table	Plywood	Yellow	20000
Chair	Plastic	Red	1500
Sofa	Stainless Steel	Silver	55000
Chair	Wooden	Light Blue	2500
Dining Table	Aluminum	Golden	65000

a) Display the details of the chair and sofa.
b) Display furniture details which price is more than 25000.
c) Display the furniture details price under 10000.
d) Display alternative rows. 
Answer:
Creating Dataframe:

import pandas as pd
def df_furniture():
    data = [['Sofa','Wooden','Maroon',25000],
            ['Dining Table','Plywood','Yellow',20000],
            ['Chair','Plastic', 'Red',1500],
            ['Sofa','Stainless Steel', 'Silver',55000],
            ['Chair','Wooden', 'Light Blue',2500],
            ['Dining Table','Aluminum', 'Golden',65000],]
    furniture=pd.DataFrame(data,columns=['Item','Material','Colour','Price'])
    print(furniture.to_string(index=False))
df_furniture()

a) print(furniture[furniture[‘Item’]==’Sofa’],”n”,furniture[furniture[‘Item’]==’Chair’]) 
b) print(furniture[furniture[‘Price’]>25000])
c) print(furniture[furniture[‘Price’]<10000])
d) print(furniture.iloc[::2])

• Create a dataframe using the 2D dictionary to store the following records:

House	Activity1	Activity2	Activity3
Blue House	98	85	88
Red House	87	76	80
Green House	59	67	91
Yellow House	78	99	55

Answer:

import pandas as pd
def df_CCA():
    data = {'House':['Blue House','Red House','Green House','Yellow House'],
            'Activity1':[98,87,59,78],
            'Activity2':[85,76,67,99],
            'Activity3':[88,80,91,55]}
    furniture=pd.DataFrame(data)
    print(furniture.to_string(index=False))
df_CCA() 

• Create a dataframe using the 2d dictionary with values as dictionary objects which store data of CCA activities for two terms:

	Red	Green	Blue	Yellow
Term1	200	350	400	380
Term2	189	250	375	300

import pandas as pd
def df_CCA():
    r={'Term1':200,'Term2':189}
    g={'Term1':350,'Term2':250}
    b={'Term1':400,'Term2':400}
    y={'Term1':380,'Term2':300}
    c={'Red':r,'Green':g,'Blue':b,'Yellow':y}
    df=pd.DataFrame(c)
    print(df)
df_CCA()

• Create a dataframe using the following matrix by ndarray, assign row, and column labels like MS excel.

	A	B	C
1	98	56	87
2	32	76	65
3	55	99	88

import pandas as pd
import numpy as np
def df_ndArray():
    nda=np.array([[98,56,97],[32,76,65],[55,99,88]])
    df=pd.DataFrame(nda,columns=['A','B','C'],index=[1,2,3])
    print(df)
df_ndArray()

Download the python code used in this post.