Question Answers
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- 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]
import pandas as pddef 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 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.
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- 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()