8.Write a Pandas program to select the name of persons whose height is between 5 to 5.5 (both values inclusive)
'name': ['Asha', 'Radha', 'Kamal', 'Divy', 'Anjali'],
'height': [ 5.5, 5, np.nan, 5.9, np.nan],
'age': [11, 23, 22, 33, 22]
import pandas as pd
data={
'name': ['Asha', 'Radha', 'Kamal', 'Divy', 'Anjali'],
'height': [ 5.5, 5, np.nan, 5.9, np.nan],
'age': [11, 23, 22, 33, 22]
}
df=pd.DataFrame(data=data)
print(df)
df[(df["height"]>=5) & (df["height"]<=5.5) &(df["height"].notna())]
9.Four series objects temp1, temp2, temp3 and temp4 store the temperature of week1, week2, week3 and week4 respectively. Create a a dataframe from these four series objects where the indexes should be “Sunday”,”Monday”,……..,”Saturday” and columns should be ‘week1’, ‘week2’,’week3’ and ‘week4’.
import pandas as pd
tmp1=pd.Series([32,31,30,31,30,33,32])
tmp2=pd.Series([32,31,30,31,30,33,32])
tmp3=pd.Series([32,31,30,31,30,33,32])
tmp4=pd.Series([32,31,30,31,30,33,32])
data={"week1":tmp1,"week2":tmp2,"week3":tmp3,"week4":tmp4}
df=pd.DataFrame(data)
df.index=["sunday","monday","tuesday","wednesday","thusday","friday","satrday"]
print(df)
10.From the dataframe object created in previous question, write a script to calculate :
a)Average temperature for each day of week I.e. average temperature for Mondays, Tuesday and so on
b)Average temperature per week
c)Average temperature of whole month
import pandas as pd
import numpy as np
tmp1=pd.Series([32,31,30,31,30,33,32])
tmp2=pd.Series([32,31,30,31,30,33,32])
tmp3=pd.Series([32,31,30,31,30,33,32])
tmp4=pd.Series([32,31,30,31,30,33,32])
data={"week1":tmp1,"week2":tmp2,"week3":tmp3,"week4":tmp4}
df=pd.DataFrame(data)
df.index=["sunday","monday","tuesday","wednesday","thusday","friday","satrday"]
print(df)
print()
print("Average temperature day wise: \n",df.mean(axis=1))
print()
print("Average temperature wise: \n",df.mean())
print()
print("Average temperature of whole month: \n",np.mean(df.mean()))
11.Given three dataframes namely Sales15, sales16 and Sales17 storing quarter wise sales of items ‘Beverages’, ‘Milk’, ‘Snack items’, ‘Icecreams’ and ‘Bakery items’. Write a script that prints the following:
a)Maximum yearly sales item-wise
b)Maximum Quarterly sales for an item across three years(eg. ‘Snack item’ had maximum sales in Quarter 3 of year 2017)
c)Average sales Quarter-wise for all items year wise.
d)Average sales Quarter-wise for all items for all years.
import pandas as pd
import numpy as np
sales15dt={ 'Beverages':[12,45,49,48],
'Milk':[12,13,14,15],
'snack items':[45,48,47,49],
'icecream':[23,24,25,26],
'bakery items':[36,35,39,34]}
dfsales15=pd.DataFrame(data=sales15dt,index=['qrt1','qtr2','qtr3','qtr4'])
sales16dt={ 'Beverages':[13,40,48,48],
'Milk':[12,18,14,17],
'snack items':[45,58,47,79],
'icecream':[23,24,2,26],
'bakery items':[3,35,39,34]}
dfsales16=pd.DataFrame(data=sales16dt,index=['qrt1','qtr2','qtr3','qtr4'])
sales17dt={ 'Beverages':[12,45,49,48],
'Milk':[78,13,17,15],
'snack items':[45,48,47,39],
'icecream':[25,24,25,26],
'bakery items':[37,35,39,34]}
dfsales17=pd.DataFrame(data=sales17dt,index=['qrt1','qtr2','qtr3','qtr4'])
print("Sales of 2015: \n\n",dfsales15);print()
print("Sales of 2016: \n\n",dfsales16);print()
print("Sales of 2017: \n\n",dfsales17);print()
# Maximum yearly sales item-wise----------------------------------------------------------------------
print("------------------------------Maximum yearly sales item-wise------------------------------")
data1={"2015":dfsales15.max(), "2016":dfsales16.max(), "2017":dfsales17.max()}
df1=pd.DataFrame(data1)
for i,j in df1.iterrows():
print("--------------------------------------------------------------------------------------")
print("item: ",i)
print(j[j.values==j.max()])
print("------------------------------Maximum quaterly sales item-wise------------------------------")
1.The table shows passenger car fuel rates in miles per gallon for several years. Make a LINE GRAPH of the data. During which year period did the fuel rate decrease?
YEAR: 2000 2002 2004 2006
RATE: 21.0 20.7 21.2 21.6
import matplotlib.pyplot as plt
yr=[2000,2002,2004,2006]
rt=[21.0,20.7,21.2,21.6]
plt.plot(yr,rt)
plt.show()
2.Draw the histogram based on the Production of Wheat in different Years
Year: 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018
Production': 4, 6, 7, 15, 24, 2, 19, 5, 16, 4
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
data={'Year':[2000,2002,2004,2006,2008,2010,2012,2014,2016,2018],
'Production':[4,6,7,15,24,2,19,5,16,4]}
d=pd.DataFrame(data)
print(d)
x=d.hist(column='Production',bins=5,grid=True)
plt.show(x)
3.The number of bed-sheets manufactured by a factory during five consecutive weeks is given below.
Week First Second Third Fourth Fifth
Number of Bed-sheets 600 850 700 300 900
Draw the bar graph representing the above data
import matplotlib.pyplot as plt
week=["First","Second","Third","Fourth","Fifth"]
num=[600,850,700,300,900]
plt.title("production by factory")
plt.xlabel("week")
plt.ylabel("NO of Bed Sheets")
plt.bar(week,num)
plt.show()
4.The number of students in 7 different classes is given below. Represent this data on the bar graph.
Class 6th 7th 8th 9th 10th 11th 12th
Number_of Students 130 120 135 130 150 80 75
import matplotlib.pyplot as plt
cl=[ "6th", "7th", "8th", "9th", "10th", "11th", "12th"]
no=[130, 120, 135, 130, 150, 80, 75]
plt.ylabel("Number_of Students")
plt.xlabel("Class")
plt.bar(cl,no)
plt.show()
5.Write a python program to draw line chart from the given financial data of ABC Co. For 5 days in the form a DataFrame namely fdf as shown below.
Day1 Day2 Day3 Day4 Day5
0 74.25 56.03 59.30 69.00 89.65
1 76.06 68.71 72.07 78.47 79
2 69.50 62.89 77.65 65.53 80.75
3 7.55 56.42 66.46 76.85 85.08
also save the graph with the name fin.png
import pandas as pd
import matplotlib.pyplot as plt
data={"day1":[74.25, 76.06, 69.50,7.55],
"day2":[56.03, 68.71, 62.89, 56.42],
"day3":[59.30, 72.07, 77.65,66.46],
"day4":[69.00, 78.47, 65.53, 76.85],
"day5":[89.65, 79, 80.75, 85.08]
}
fdf=pd.DataFrame(data)
fdf.plot()
plt.savefig("fin.png")
