# Creating Dataframes Questions and Answers (Informatics Practices class 12)

import pandas as pd
def df_SingleColumn():
l = [33,55,66,39,45]
df = pd.DataFrame(l)
print(df)
df_SingleColumn()
• 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 pddef df_data():   df1=pd.DataFrame([22,56,78])   df2=pd.DataFrame([[11,43,67]])   print(df1)   print(df2)df_data()`

`    00  221  562  78    0   1   20  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 pddef 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.
Creating Dataframe:
`import pandas as pddef 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

`import pandas as pddef 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() `
`import pandas as pddef 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()`
`import pandas as pdimport numpy as npdef 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()`