1. Write code to create
a Series object using the Python sequence [11,21,31,41]. Assume that Pandas is
imported as alias name pd.
Solution:
import
pandas as pd
s1=pd.Series([11,21,31,41])
print(“Series
object 1”)
print(s1)
Output
Series
object 1
0 11
1 21
2 31
3 41
dtype:
int64
2.
Write code to create a Series object using the Python
sequence (1,2,3,4). Assume that Pandas is imported as alias name pd.
Solution:
import
pandas as pd
s1=pd.Series((1,2,3,4))
print(“Series
object 1”)
print(s1)
Output
Series
object 1
0 1
1 2
2 3
3 4
dtype:
int64
3.
Write a program to create a Series object using
individual characters ‘o’,’h’, and ‘o’. Assume that Pandas is imported alias
name pd.
Solution:
import pandas as pd
print("Series
object")
s4=pd.Series(['o','h','o'])
print(s4)
Output
Series
object
0 o
1 h
2 o
dtype:
object
4. Write a program to
create a Series object using a string: “So funny”. Assume that Pandas is
imported as alias name pd.
Solution:
import pandas as pd
print("Series
object")
s4=pd.Series("So
funny")
print(s4)
Output
Series
object
0 So funny
dtype:
object
5. Write a program to
create a Series object using three different words : “I”, “am”, “laughing”.Assume
that Pandas is imported as alias name pd.
Solution:
import
pandas as pd
print("Series
object")
s5=pd.Series(["I",
"am", "laughing"])
print(s5)
Output
Series
object
0 I
1 am
2 laughing
dtype:
object
6.
Write a program to create a Series object using an
ndarray that has 5 elements in the ranges 24 to 64.
Solution:
import
pandas as pd
import
numpy as np
s6=pd.Series(np.linspace(24,64,5))
print(s6)
Output
0 24.0
1 34.0
2 44.0
3 54.0
4 64.0
dtype:
float64
7. Write a program to
create a Series object using an ndarray that is created by tiling a list
[3,5], twice.
Solution:
import pandas as pd
import numpy as np
s7=pd.Series(np.tile([3,5],2))
print(s7)
Output
0 3
1 5
2 3
3 5
dtype:
int32
8. Write a program to
create a Series object using a dictionary that stores the number of students in
each section of class 12 in your school.
Solution:
import pandas as pd
import numpy as np
stu={'Vanya':65,'Sanya':87,'Manya':89}
s8=pd.Series(stu)
print(s8)
Output
Vanya 65
Sanya 87
Manya 89
dtype:
int64
9. Write a program to
create a Series object that stores initial budget allocated (50000/- each) for
the four quarters of the years: Qtr1, Qtr2, Qtr3 and Qtr4.
Solution:
import
pandas as pd
s9=pd.Series(50000,index=['Qtr1','Qtr2','Qtr3','Qtr4'])
print(s9)
Output
Qtr1 50000
Qtr2 50000
Qtr3 50000
Qtr4 50000
dtype:
int64
10.Total number of
medals to be won is 200 in the Inter University games held every alternate
year. Write code to create a Series object that these medals for games to be held
in the decade 2020-2029.
Solution:
import pandas as pd
s10=pd.Series(200,
index=range(2020,2029,2))
print(s10)
Output
2020 200
2022 200
2024 200
2026 200
2028 200
dtype:
int64
11.A Python list namely section
stores the section names (‘A’, ‘B’, ‘C’, ‘D’) of class 12 in your school.
Another list contri stores the contribution made by these students to a
charity fund endorsed by the school. Write code to create a Series object that
stores the contribution amount as the values and the section names as the
indexes.
Solution:
import pandas as pd
section=['A','B','C','D']
contri=[6700,5600,5000,5200]
s11=pd.Series(data=contri,index=section)
print(s11)
Output
A 6700
B 5600
C 5000
D 5200
dtype:
int64
12.Sequence section and
contri1 store the section names (‘A’, ‘B’, ‘C’, ‘D’) and contribution made by
them respectively (6700, 5600, 5000, 5200, nil) for a charity. Your school has
decided to donate as much contribution as made by each section, i.e. the donation
will be doubled.
Solution :
import pandas as pd
import numpy as np
section=['A','B','C','D','E']
contri1=np.array([6700,5600,5000,5200,np.NaN])
s12=pd.Series(data=contri1*2,index=section,
dtype=np.float32)
print(s12)
Output
A 13400.0
B 11200.0
C 10000.0
D 10400.0
E NaN
dtype:
float32
13.Consider the rows
series objects s11 and s12 that you created in examples 11 and 12
respectively. Print the attributes of both these objects in a report form as
shown below:
Attribute name Object s11 Object s12
Data type
Shape
No. of bytes
No. of dimensions
Item size
Has NaNs?
Empty?
Solution:
14.Consider a Series
object s8 that stores the number of students in each section of class 12 (as
shown below)
Vanya 65
Sanya 87
Manya 89
Solution:
import
pandas as pd
stu={'Vanya':65,'Sanya':87,'Manya':89}
s8=pd.Series(stu)
print("Tickets
amount :")
print(s8[:2]*100)
Output
Tickets
amount :
Vanya 6500
Sanya 8700
dtype:
int64
15.Consider the Series
object s13 that stores the contribution of each section, as shown below:
Solution:
A 13400.0
B 11200.0
C 10000.0
D 10400.0
E NaN
Solution:
import pandas as pd
import numpy as np
section=['A','B','C','D','E']
contri1=np.array([6700,5600,5000,5200,np.NaN])
s12=pd.Series(data=contri1*2,index=section,
dtype=np.float32)
print(s12)
s12[0]=7600
s12[3:]=7000
print("Series
object after modifying amounts ")
print(s12)
16.A Series object trdata
consist of around 2500 rows of data.
Write a program to print the following details :
(i)
First
100 rows of data (ii)
Last 5 rows of data
Solution:
import pandas as pd
print(trdata.head(100))
print(trdata.tail())
17.Number of students in
classes 11 and 12 in three streams (‘Science’, ‘Commerce’, ‘Humanities’) are
stored in two Series objects c11 and 12. Write code to find total number of
students in classes 11 and 12, stream wise.
Solution:
import pandas as pd
import numpy as np
c11=pd.Series(data=[30,40,50],index=['Science',
'Commerce', 'Humanities'])
c12=pd.Series(data=[37,44,45],index=['Science',
'Commerce', 'Humanities'])
print("Total no.
of students")
print(c11+c12)
Output
Total
no. of students
Science 67
Commerce 84
Humanities 95
dtype:
int64
18.Object1 Population stores the details of
population in four metro cities of India and Object2 AvgIncome stores the total average income reported in previous year
in each of these metros. Calculate income per capita for each of these metro
cities.
Solution:
import pandas as pd
Population=pd.Series([21435432,32435432,21325465,21436576]),\
index=[‘Delhi’, ‘Mumbai’, ‘Kolkata’,
‘Chennai’])
AvgIncome=pd.Series([32436587437632,21435467984321,43215487094321,21325476874321],\
index=[‘Delhi’,
‘Mumbai’, ‘Kolkata’, ‘Chennai’])
perCapita=AvgIncome/Population
print(“Population in
for metro cities ”)
print(Population)
print(“Avg. Income in
four metro cities”)
print(AvgIncome)
print(“Per Capita
Income in four metro cities”)
print(perCapita)
Output:
Population
in for metro cities
Delhi 21435432
Mumbai 32435432
Kolkata 21325465
Chennai 21436576
dtype:
int64
Avg.
Income in four metro cities
Delhi 32436587437632
Mumbai 21435467984321
Kolkata 43215487094321
Chennai 21325476874321
dtype:
int64
Per
Capita Income in four metro cities
Delhi 1.513223e+06
Mumbai 6.608658e+05
Kolkata 2.026473e+06
Chennai 9.948173e+05
dtype:
float64
19.What will be the
output produced by the following program?
import pandas as pd
info=pd.Series(data=[31,41,51])
print(info)
print(info>40)
print(info[infor>40])
Solution:
20.Series object s11
stores the charity contribution made by each section (see below):
A 6700
B 5600
C 5000
D 5200
Write a
program to display which section made a contribution more than Rs. 5500/-
Solution:
import pandas as pd
print(“Contribution>5000 by :”)
print(s11[s11>5500])
21.Given a dictionary
that stores the section names’ list as
value for ‘Section’ key and
contribution amounts’ list as value for ‘Contri’ key:
Dict1={‘Section’:[‘A’, ‘B’, ‘C’, ‘D’],
‘Contri’:[6700,5600, 5000, 5200]}
Write code to create and display the data
frame using above dictionary.
Solution:
import
pandas as pd
Dict1={‘Section’:[‘A’,
‘B’, ‘C’, ‘D’], ‘Contri’:[6700,5600, 5000, 5200]}
Df1=pd.DataFrame(Dict1)
print(df1)
22.Create and display a DataFrame
from a 2D dictionary, Sales, which stores the quarter-wise sales as inner
dictionary for two years, as show below:
Sales={‘yr1’:{‘Qtr1’:34500, ‘Qtr2’:56000,
‘Qtr3’:47000, ‘Qtr4’:49000 },
{‘yr2’:
{‘Qtr1’:47500, ‘Qtr2’:46100, ‘Qtr3’:56000, ‘Qtr4’:59000 }}
Solution:
import pandas as pd
Sales={‘yr1’:{‘Qtr1’:34500,
‘Qtr2’:56000, ‘Qtr3’:47000, ‘Qtr4’:49000 },
{‘yr2’: {‘Qtr1’:47500, ‘Qtr2’:46100,
‘Qtr3’:56000, ‘Qtr4’:59000 }}
dfSales=pd.DataFrame(Sales)
print(dfSales)
23.Carefully read the
following code.
import
pandas as pd
yr1={‘Qtr1’
:44900, ‘Qtr2’: 46100, ‘Q3’:57000, ‘Q4’:59000}
yr2={‘A’:54500,
‘B’ :51000, ‘Qtr4’:57000}
disSales1={1:yr1,
2:yr2}
df3=pd.DataFrame(diSales1)
(i)
List
the index labels of the DataFrame df3
(ii)
List
the column names of DataFrame df3.
Solution :
(i)
The
index labels of df3 will include : A, B, Q3, Q4, Qtr1, Qtr2, Qtr4.
The
total number of indexes is equal total
unique inner keys, i.e., 7.
(ii)
The
column name of df3 will be: 1,2
24.Write a program to
create a dataframe from a list containing dictionaries of the sales performance
of four zonal offices. Zones names should be the should be the row labels.
Solution:
import pandas as pd
zoneA={‘Target’:56000,
‘Sales’:58000}
zoneB={‘Target’:70000,
‘Sales’:68000}
zoneC={‘Target’:75000,
‘Sales’:78000}
zoneD={‘Target’:60000,
‘Sales’:61000}
sales=[zoneA, zoneB,
zoneC, zoneD]
salesDf=pd.DataFrame(Sales,
index=[‘zoneA’, ‘zoneB’, ‘zoneC’,
‘zoneD’])
print(saleDf)
25.Write a program to
create a dataframe from a 2D list. Specify own index labels.
Solution:
Import pandas as pd
List2=[[25,45,60],[34,67,89],[88,90,56])
Df2=pd.DataFrame(List2,
index=[‘row1’, ‘row2’, ‘row3’])
print(Df2)
26.Write a program to
create a dataframe from a list containing 2 lists, each contain Target and
actual Sales figures of four zonal offices. Give appropriate row labels.
Solution:
Import
pandas as pd
Target=[50000,56000,43000,78000]
Sales=[58000,
68000, 78000, 61000]
ZoneSales=[
Target, Sales]
zsaleDf=pd.DataFrame(ZoneSales,
columns=[[‘zoneA’, ‘zoneB’, ‘zoneC’,
‘zoneD’], \
index=[‘
Target’, ‘Sales’])
print(zsalesDf)
27.What will be the
output of following code?
import pandas as pd
import numpy as np
Arr1=np.array([[11,12],[13,14],[15,16]], np.int32)
Dtf2=pd.DataFrame(arr1)
Solution:
0
1
0 11 12
1 13 14
2 15 16
28.What a program to create a DataFrame from a 2D array as shown below:
101 113 124
130 140 200
115 216 217
Solution:
import pandas as pd
import numpy as np
arr2=np.array([[101,113,124],[130,140,200],[115,216,217]])
df=pd.DataFrame(arr2)
print(df)
Output:
0
1 2
0 101
113 124
1 130
140 200
2 115
216 217
29. Consider two series
objects staff and salaries that store the number of people in
various office branches and salaries distributed in these branches,
respectively.
Write a program to create another Series
object that stores average salary per branch and then create a DataFrame object
from these Series objects.
Solution:
import
pandas as pd
import
numpy as np
staff=pd.Series([20,36,44])
salaries=pd.Series([234000,542000,763000])
avg=salaries/staff
org={'people':staff,'Amount':salaries,
'Average':avg}
df=pd.DataFrame(org)
print(df)
Output:
people Amount
Average
0 20 234000
11700.000000
1 36 542000
15055.555556
2 44 763000
17340.909091
30. Write a program to
create a DataFrame to store weight, age and names of 3 people. Print the
DataFrame and its transpose.
Solution:
import pandas as pd
import numpy as np
df=pd.DataFrame({'Weight':[43,65,76],\
'Name':['Amit','Aviral','Ankit'],\
'Age':[30,19,21]})
print('Original DataFrame')
print(df)
print('Transepose')
print(df.T)
Output:
Original DataFrame
Weight Name
Age
0 43 Amit
30
1 65 Aviral
19
2 76 Ankit
21
Transepose
0 1 2
Weight
43 65 76
Name
Amit Aviral Ankit
Age
30 19 21
31.Given a DataFrame namely aid that stores the aid by NGOs for different states:
|
|
Toys |
Books |
Uniform |
Shoes |
|
Andhra |
7912 |
6532 |
986 |
8765 |
|
Odisha |
9854 |
5432 |
543 |
7654 |
|
M.P. |
5465 |
4356 |
342 |
6787 |
|
U.P. |
8798 |
9867 |
768 |
9865 |
Write a
program to display the aid for
(i)
Books
and Uniform only
(ii)
Shoes
only
Solution:
import pandas as pd
aid=pd.DataFrame({'Toys':[7912, 6532, 986, 8765,],\
'Books':[9854, 5432, 543, 7654],\
'Uniform':[5465, 4356, 342, 6787],\
'Shoes':[8798, 9867, 768, 9865]}, \
index=['Andhra','Odisha','M.P.','U.P.'])
print("Aid for
books and uniform")
print(aid[['Books','Uniform']])
print('Aid for
shoes:')
print(aid.Shoes)
Output :
Aid for
books and uniform
Books Uniform
Andhra 9854 5465
Odisha 5432 4356
M.P. 543 342
U.P. 7654 6787
Aid for
shoes:
Andhra 8798
Odisha 9867
M.P. 768
U.P. 9865
Name:
Shoes, dtype: int64
32.Given a DataFrame namely aid that stores the aid by NGOs for different states:
|
|
Toys |
Books |
Uniform |
Shoes |
|
Andhra |
7912 |
6532 |
986 |
8765 |
|
Odisha |
9854 |
5432 |
543 |
7654 |
|
M.P. |
5465 |
4356 |
342 |
6787 |
|
U.P. |
8798 |
9867 |
768 |
9865 |
Write a
program to display the aid for states ‘Andhra’ and ‘Odisha’ for Books and
Uniform only.
Solution:
import pandas as pd
aid=pd.DataFrame({'Toys':[7912, 6532, 986, 8765,],\
'Books':[9854, 5432, 543, 7654],\
'Uniform':[5465, 4356, 342, 6787],\
'Shoes':[8798, 9867, 768, 9865]}, \
index=['Andhra','Odisha','M.P.','U.P.'])
print(aid.loc['Andhra':'Odisha','Books':'Uniform'])
Output:
Books
Uniform
Andhra 9854
5465
Odisha 5432
4356
33. Consider the
following dataframe saleDf:
Target Sales
zoneA 56000 58000
zoneB 70000 68000
zoneC 75000 78000
zoneD 60000 61000
write a
program to add a column namely Orders
having values 6000,6700,6000 respectively for the zones A,B,C and D. The
program should also add a new for a new zone zoneE. Add some dummy values in this row.
Solution:
import pandas as pd
saleDf=pd.DataFrame({'Target
':[56000,70000,75000,60000],\
'Sales':[58000,68000,78000,61000]},\
index=['zoneA','zoneB','zoneC','zoneD'])
saleDf['Orders']=[6000,6700,6200,6000]
saleDf.loc['zoneE',:]=[50000,45000,5000]
print(saleDf)
Output:
Target Sales
Orders
zoneA 56000.0
58000.0 6000.0
zoneB 70000.0
68000.0 6700.0
zoneC 75000.0
78000.0 6200.0
zoneD 60000.0
61000.0 6000.0
zoneE 50000.0
45000.0 5000.0
34.From the df used
above, create another DataFrame and it must not contain the column ‘Population’
and the row Bangalore.
Population Hospital
Schools
Delhi
21436576 324
4322
Mumbai
21436587 657
5432
Kolkata
76437632 245
6732
Chennai
43763254 763
3467
Banglore
98658798 879
4356
Solution:
import pandas as pd
Adict={'Population':[21436576,21436587,76437632,43763254,98658798],'Hospital':[324,657,245,763,879],\
'Schools':[4322,5432,6732,3467,4356]}
df=pd.DataFrame(Adict,columns=['Population','Hospital','Schools'],index=['Delhi','Mumbai','Kolkata','Chennai','Banglore'])
del df['Population']
df1=df.drop(['Banglore'])
print(df1)
Output:
Hospital Schools
Delhi 324 4322
Mumbai 657 5432
Kolkata 245 6732
Chennai 763 3467
35.Consider the
following dataframe saleDf:
Target Sales
zoneA 56000 58000
zoneB 70000 68000
zoneC 75000 78000
zoneD 60000 61000
write a program to rename indexes of ‘zoneC’,
and ‘zoneD’ as ‘Central’ and ‘Daksin’ respectively and the column names
‘Target’ and ‘Sales’ as ‘Targeted’ and ‘Acieved’ respectively.
Solution:
import pandas as pd
saleDf=pd.DataFrame({'Target
': [56000,70000,75000,60000],\
'Sales':[58000,68000,78000,61000]},\
index=['zoneA','zoneB','zoneC','zoneD'])
saleDf['Orders']=[6000,6700,6200,6000]
print(saleDf.rename(index={'zoneC':'Central','zoneD':'Daksin'},\
columns={'Target':'Targeted','Sales':'Acieved'}))
Output:
Target Acieved
Orders
zoneA 56000
58000 6000
zoneB 70000
68000 6700
Central 75000
78000 6200
Daksin 60000
61000 6000
36.Will the program
reflect the renamed indexes and columns names in the dataframe saleDf? Make Changes in the previous program so that dataframe saleDf has the changed indexes and
columns.
Solution:
import pandas as pd
saleDf=pd.DataFrame({'Target
':[56000,70000,75000,60000 ],\
'Sales':[58000,68000,78000,61000]},\
index=['zoneA','zoneB','zoneC','zoneD'])
saleDf['Orders']=[6000,6700,6200,6000]
saleDf.rename(index={'zoneC':'Central','zoneD':'Daksin'},\
columns={'Target':'Targeted','Sales':'Achieved'},inplace=True)
print(saleDf)
Output:
Target Acieved
Orders
zoneA 56000
58000 6000
zoneB 70000
68000 6700
Central 75000
78000 6200
Daksin 60000
61000 6000
37.Given a Series that
stores the area of some states in km2. Given series has been created
like this:
Ser1=pd.Series([32542,543,564,76876,54768,87643,768765,546567,548765,7676,4354,767,343,768753])
(a) Write code to find
out the biggest and smallest areas from the given series.
(b) From Ser1 of areas
(given earlier that stores areas of states in km2), find out the
areas that are more that 50000 km2.
Solution:
import pandas as pd
import numpy as np
Ser1=pd.Series([32542,543,564,76876,54768,87643,768765,546567,\
548765,7676,4354,767,343,768753])
print("Top 3
biggest areas are:")
print(Ser1.sort_values().tail(3))
print("3
smallest areas are :")
print(Ser1.sort_values().head(3))
print(Ser1[Ser1>50000])
Output:
Top 3
biggest areas are:
8 548765
13 768753
6 768765
dtype:
int64
3
smallest areas are :
12 343
1 543
2 564
dtype:
int64
3 76876
4 54768
5 87643
6 768765
7 546567
8 548765
13 768753
dtype:
int64
38.Write a program to
create a Series object with 6 random integers and having indexes as
:[‘p’,’q’,’r’,’s’,’u’,’t’]
Solution:
import pandas as pd
import numpy as np
s=pd.Series(np.random.randint(6),index=['p','q','r','s','u','t'])
print(s)
39. Write a program to
create
(a) data series s1
and then change the indexes of the Series object in any random order.
(b) To sort the values of
a Series object s1 in ascending order of its values and store in into series
object s2.
(c) To sort the values of
a Series object s1 in ascending order of its values and store in into series
object s3.
Solution:
import pandas as pd
s1=pd.Series(data=[6100,5200,3000,4200,5500,8600])
print("Original
Data Series :")
print(s1)
s2=s1.sort_values()
print("Series
object s2 :\n",s2)
s3=s1.sort_index(ascending=False)
print("Series
object s2 :\n",s3)
Output:
Original
Data Series :
0 6100
1 5200
2 3000
3 4200
4 5500
5 8600
dtype:
int64
Series
object s2 :
2
3000
3 4200
1 5200
4 5500
0 6100
5 8600
dtype:
int64
Series
object s2 :
5
8600
4 5500
3 4200
2 3000
1 5200
0 6100
dtype:
int64
40. Given a Series
object S4. Write a program to change the values at its 2nd
row(index) and 3rd row to 8000.
Solution:
import
pandas as pd
s4=pd.Series(data=[6100,5200,3000,4200,5500,8600])
print("Original
Data Series :")
print(s4)
s4[1:3]=8000
print("Series
object s4 after changing value :\n", s4)
Output:
Original
Data Series :
0 6100
1 5200
2 3000
3 4200
4 5500
5 8600
dtype:
int64
Series
object s4 after changing value :
0
6100
1 8000
2 8000
3 4200
4 5500
5 8600
dtype: int64
41.Given a Series object
s4. Write a program to calculate the cubes of the Series values.
Solution:
import pandas as pd
s4=pd.Series(data=[5,6,9,7])
print("Original
Data Series :")
print(s4)
print(" Cubes of
s4 values :\n", s4**3)
Output:
Original
Data Series :
0 5
1 6
2 9
3 7
dtype:
int64
Cubes of s4 values :
0 125
1 216
2 729
3 343
dtype:
int64
42.Given a Series object
s5. Write a program to store the squares of the Series values in object s6.
Display s6’s values which are >15.
Solution:
import pandas as pd
s5=pd.Series(data=[2,4,6,8])
print("Original
Data Series :")
print(s5)
s6=s5*2
print("Values in
s6 > 15 :\n", s6[s6>15])
Output:
Original
Data Series :
0 2
1 4
2 6
3 8
dtype:
int64
Values
in s6 > 15 :
3 16
dtype:
int64
43.Write a program to
display number of rows and number of column in DataFrame df.
Solution:
import pandas as pd
df=pd.DataFrame({'Weight':[76,43,65,76],\
'Name':['Anshu','Amit','Aviral','Ankit'],\
'Age':[30,19,21,23]})
print('Original
DataFrame')
print(df)
print("No. of
rows :", df.shape[0])
print("No. of
columns :", df.shape[1])
Output:
Original
DataFrame
Weight Name Age
0 76
Anshu 30
1 43
Amit 19
2 65
Aviral 21
3 76
Ankit 23
No. of
rows : 4
No. of
columns : 3
44.Write a program to
display number of rows and number of columns in DataFrame df without
using shape attribute. Also display only the Weight of first and third rows.
Solution:
import pandas as pd
df=pd.DataFrame({'Weight':[76,43,65,76],\
'Name':['Anshu','Amit','Aviral','Ankit'],\
'Age':[30,19,21,23]})
print('Original
DataFrame')
print(df)
rows=len(df.axes[0])
cols=len(df.axes[1])
print("No. of
rows: ", rows)
print("No. of
columns: ", cols)
print("Display
only the Weight of first and third rows")
print(df.iloc[[0,2],[2]])
Output:
Original
DataFrame
Weight Name Age
0 76
Anshu 30
1 43
Amit 19
2 65
Aviral 21
3 76
Ankit 23
No. of
rows : 4
No. of
columns : 3
Display
only the Weight of first and third rows
Age
0 30
2 21
45. Consider Dataframe:
DataFrame :- df
|
Name |
Position |
City |
Age |
Sex |
|
Aakash |
Manager |
Delhi |
35 |
M |
|
Sunakshi |
Programmer |
Mumbai |
37 |
F |
|
Amitabh |
Manager |
Kanpur |
33 |
M |
|
Madhuri |
Programmer |
Mumbai |
40 |
F |
|
Ashish |
Manager |
Kanpur |
27 |
M |
|
Akshay |
Programmer |
Kanpur |
34 |
M |
|
Preeti |
Programmer |
Delhi |
26 |
F |
|
Govinda |
Manager |
Delhi |
30 |
M |
|
Rani |
Manager |
Mumbai |
28 |
F |
|
Shilpa |
Manager |
Kanpur |
26 |
F |
|
Ayushman |
Programmer |
Delhi |
28 |
M |
Write
the statement and output for the following (refer to the above DataFrame(df) :
a)
Display Name of Employee
b)
Display Name and Position of Manager.
c)
Display name and city of Employee those age
more than 30 and less than or equal 35
d)
Display female employee name.
e)
Display name record those who are not living
Kanpur.
f)
Display Name, position and city those who are
living Kanpur and female.
Solution:
import pandas as pd
emp={'Name':['Aakash','Sunakshi','Amitabh','Madhuri','Ashish','Akshay','Preeti','Govinda','Rani','Shilpa','Ayushman'],
'Position':['Manager','Programmer','Manager','Programmer','Manager','Programmer','Programmer','Manager','Manager','Manager','Programmer'],
'City':['Delhi','Mumbai','Kanpur','Mumbai','Kanpur','Kanpur','Delhi','Delhi','Mumbai','Kanpur','Delhi'],
'Age':[35,37,33,40,27,34,26,30,28,26,28],
'Sex':['M','F','M','F','M','M','F','M','F','F','M']}
df=pd.DataFrame(emp)
print(df)
print('a) Display
Name of Employee')
print(df['Name'])
print('b)Display Name and Position of
Manager.')
print(df[['Name','Position']])
print('c) Display
name and city of Employee those age more than 30 and less than or equal 35')
print(df[['Name','City','Age']]
[(df['Age']>30) & (df['Age']<=35)])
print('d)Display female employee name.')
print(df[['Name','Sex']][df['Sex']=='F'])
print('e)Display name record those who are not
living Kanpur.')
print(df[['Name','City']][df['City']=='Kanpur'])
print('f) Display
Name, position and city those who are living Kanpur and female.')
print(df[['Name','Position','City']][(df['City']=='Kanpur')
& (df['Sex']=='F')])
46 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.
import pandas as
pd
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))
print(furniture[furniture['Item']=='Sofa'],"n",furniture[furniture['Item']=='Chair'])
print(furniture[furniture['Price']>25000])
print(furniture[furniture['Price']<10000])
print(furniture.iloc[::2])
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