Important IP Programs

import pandas as pd

marks = [78, 92, 85, 64]
s = pd.Series(marks)
print(s)

# Output:
# 0    78
# 1    92
# 2    85
# 3    64
# dtype: int64

import pandas as pd

days = [31, 31, 30, 31]
months = ['July', 'Aug', 'Sept', 'Oct']
s = pd.Series(days, index=months)
print(s)

# Output:
# July    31
# Aug     31
# Sept    30
# Oct     31
# dtype: int64

import pandas as pd

stock = {'Pens': 150, 'Erasers': 80, 'Sharpeners': 60, 'Notebooks': 200}
Inventory = pd.Series(stock)
print(Inventory)

# Output:
# Pens          150
# Erasers        80
# Sharpeners     60
# Notebooks     200
# dtype: int64

import pandas as pd

s = pd.Series(100, index=['X1', 'X2', 'X3', 'X4', 'X5'])
print(s)

# Output:
# X1    100
# X2    100
# X3    100
# X4    100
# X5    100
# dtype: int64

import pandas as pd
import numpy as np

arr = np.array(['a', 'b', 'c'])
s = pd.Series(arr, index=['first', 'second', 'third'])
print(s)

# Output:
# first     a
# second    b
# third     c
# dtype: object

import pandas as pd

data = ['Mammal', 'Mammal', 'Fish']
indx = ['Dog', 'Cat', 'Goldfish']
s = pd.Series(data, index=indx)
print(s)

# Output:
# Dog         Mammal
# Cat         Mammal
# Goldfish      Fish
# dtype: object

import pandas as pd

di = {'Corbett': 'Uttarakhand', 'Sariska': 'Rajasthan',
      'Kanha': 'Madhya Pradesh', 'Gir': 'Gujarat'}
NP = pd.Series(di)
print(NP['Sariska'])

# Output:
# Rajasthan

import pandas as pd

list1 = [-10, -20, -30]
ser = pd.Series(list1 * 2)
print(ser)

# Output:
# 0   -10
# 1   -20
# 2   -30
# 3   -10
# 4   -20
# 5   -30
# dtype: int64

import pandas as pd

stud = {'Name': 'Ramya', 'Class': 11, 'House': 'Red'}
s = pd.Series(stud)
print(s)

# Output:
# Name     Ramya
# Class       11
# House      Red
# dtype: object

import pandas as pd

s = pd.Series([10, 20, 30, 40, 50, 60, 70])
print(s.head(3))
print(s.tail(2))

# Output:
# 0    10
# 1    20
# 2    30
# dtype: int64
# 5    60
# 6    70
# dtype: int64

import pandas as pd

s = pd.Series([45, 12, 15], index=['mouse', 'printer', 'webcam'])
print(s.index)
print(s.values)
print(s.size)
print(s.dtype)

# Output:
# Index(['mouse', 'printer', 'webcam'], dtype='object')
# [45 12 15]
# 3
# int64

import pandas as pd

s = pd.Series([45, 12, 15, 200], index=['mouse', 'printer', 'webcam', 'keyboard'])
print(s.loc['webcam'])    # by label
print(s.iloc[0])          # by position: the first value
print(s[['mouse', 'keyboard']])

# Output:
# 15
# 45
# mouse        45
# keyboard    200
# dtype: int64

import pandas as pd

com = pd.Series([45, 12, 15, 200], index=['mouse', 'printer', 'webcam', 'keyboard'])
print(com[1:3])

# Output:
# printer    12
# webcam     15
# dtype: int64

import pandas as pd

com = pd.Series([45, 12, 15, 200], index=['mouse', 'printer', 'webcam', 'keyboard'])
print(com['printer':'keyboard'])
print(com[::-1])          # all values in reverse order

# Output:
# printer      12
# webcam       15
# keyboard    200
# dtype: int64
# keyboard    200
# webcam       15
# printer      12
# mouse        45
# dtype: int64

import pandas as pd

marks = pd.Series([78, 92, 45, 85, 33], index=['Riya', 'Aman', 'Sara', 'Kabir', 'Neha'])
print(marks[marks >= 80])
print(marks[marks < 40])

# Output:
# Aman     92
# Kabir    85
# dtype: int64
# Neha    33
# dtype: int64

import pandas as pd

s = pd.Series([12, 45, 67, 23, 89, 34, 90, 56])
q75 = s.quantile(0.75)
print("75th percentile:", q75)
print(s[s > q75])

# Output:
# 75th percentile: 72.5
# 4    89
# 6    90
# dtype: int64

import pandas as pd

price = pd.Series([100, 250, 80], index=['Pen', 'Bag', 'Ruler'])
print(price + 10)
print(price * 2)

# Output:
# Pen      110
# Bag      260
# Ruler     90
# dtype: int64
# Pen      200
# Bag      500
# Ruler    160
# dtype: int64

import pandas as pd

marks = pd.Series([78, 92, 85, 64])
print("Total:", marks.sum())
print("Average:", marks.mean())
print("Highest:", marks.max())
print("Lowest:", marks.min())

# Output:
# Total: 319
# Average: 79.75
# Highest: 92
# Lowest: 64

import pandas as pd

term1 = pd.Series([40, 35, 45], index=['Riya', 'Aman', 'Sara'])
term2 = pd.Series([42, 30, 48], index=['Riya', 'Aman', 'Sara'])
print(term1 + term2)

# Output:
# Riya    82
# Aman    65
# Sara    93
# dtype: int64

import pandas as pd

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5, 6], index=['b', 'c', 'd'])
print(s1 + s2)

# Output:
# a    NaN
# b    6.0
# c    8.0
# d    NaN
# dtype: float64

import pandas as pd

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5, 6], index=['b', 'c', 'd'])
print(s1.add(s2, fill_value=0))

# Output:
# a    1.0
# b    6.0
# c    8.0
# d    6.0
# dtype: float64

import pandas as pd

df = {"Technology": ["Programming", "Robotics", "3D Printing"],
      "Time (in months)": [4, 4, 3]}
df = pd.DataFrame(df)
print(df)

# Output:
#     Technology  Time (in months)
# 0  Programming                 4
# 1     Robotics                 4
# 2  3D Printing                 3

import pandas as pd

data = [["Divya", "HR", 95000], ["Mamta", "Marketing", 97000],
        ["Payal", "IT", 980000], ["Deepak", "Sales", 79000]]
df = pd.DataFrame(data, columns=["Name", "Department", "Salary"])
print(df)

# Output:
#      Name Department  Salary
# 0   Divya         HR   95000
# 1   Mamta  Marketing   97000
# 2   Payal         IT  980000
# 3  Deepak      Sales   79000

import pandas as pd

data = [{'Student': 'Aarav', 'Marks': 85},
        {'Student': 'Aisha', 'Marks': 90},
        {'Student': 'Vivaan', 'Marks': 78},
        {'Student': 'Meera', 'Marks': 92}]
df = pd.DataFrame(data)
print(df)

# Output:
#   Student  Marks
# 0   Aarav     85
# 1   Aisha     90
# 2  Vivaan     78
# 3   Meera     92

import pandas as pd

voters = [{'Voter_Name': 'Arjun', 'Voter_Age': 35},
          {'Voter_Name': 'Bala', 'Voter_Age': 23},
          {'Voter_Name': 'Govind', 'Voter_Age': 25},
          {'Voter_Name': 'Dhruv', 'Voter_Age': 19},
          {'Voter_Name': 'Navya', 'Voter_Age': 18}]
labels = ['Ar1001', 'Ba3002', 'Go4002', 'Dh4007', 'Na6005']
df = pd.DataFrame(voters, index=labels)
print(df)

# Output:
#        Voter_Name  Voter_Age
# Ar1001      Arjun         35
# Ba3002       Bala         23
# Go4002     Govind         25
# Dh4007      Dhruv         19
# Na6005      Navya         18

import pandas as pd

dict1 = {'a': 10, 'b': 20}
dict2 = {'a': 5, 'b': 10, 'c': 20}
df = pd.DataFrame([dict1, dict2])
print(df)

# Output:
#     a   b     c
# 0  10  20   NaN
# 1   5  10  20.0

import pandas as pd

data = {'Items': pd.Series(['Bread', 'Milk', 'Eggs']),
        'Quantity': pd.Series([50, 100, 120]),
        'Unit_Price': pd.Series([30, 25, 6])}
StoreDF = pd.DataFrame(data)
print(StoreDF)

# Output:
#    Items  Quantity  Unit_Price
# 0  Bread        50          30
# 1   Milk       100          25
# 2   Eggs       120           6

import pandas as pd

S1 = pd.Series(['Rubina', 'Jaya', 'Vaibhav'], index=[10, 16, 18])
S2 = pd.Series(['IP', 'HSc', 'IP'], index=[10, 16, 18])
S3 = pd.Series([56, 67, 86], index=[10, 16, 18])
xiia = {'Name': S1, 'Subject': S2, 'Marks': S3}
df = pd.DataFrame(xiia)
print(df)

# Output:
#        Name Subject  Marks
# 10   Rubina      IP     56
# 16     Jaya     HSc     67
# 18  Vaibhav      IP     86

import pandas as pd

result = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'],
                       'Marks': [88, 75, 93],
                       'Grade': ['A', 'B', 'A']})
print("Row labels:", result.index)
print("Column labels:", result.columns)
print(result.dtypes)
print("Shape (rows, columns):", result.shape)
print("Dimensions:", result.ndim)
print("Total values:", result.size)

# Output:
# Row labels: RangeIndex(start=0, stop=3, step=1)
# Column labels: Index(['Name', 'Marks', 'Grade'], dtype='object')
# Name     object
# Marks     int64
# Grade    object
# dtype: object
# Shape (rows, columns): (3, 3)
# Dimensions: 2
# Total values: 9

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara', 'Kabir'],
                   'Marks': [88, 75, 93, 67]})
print(df.head(2))
print(df.tail(1))
print(df.T)

# Output:
#    Name  Marks
# 0  Riya     88
# 1  Aman     75
#     Name  Marks
# 3  Kabir     67
#           0     1     2      3
# Name   Riya  Aman  Sara  Kabir
# Marks    88    75    93     67

import pandas as pd

df = pd.DataFrame({'Store': ['Store1', 'Store2', 'Store3'],
                   'Qtr1': [300, 350, 250], 'Qtr2': [240, 340, 180],
                   'Qtr3': [450, 403, 145], 'Qtr4': [230, 210, 160]})
print(df.size)
print(df[1:3])

# Output:
# 15
#     Store  Qtr1  Qtr2  Qtr3  Qtr4
# 1  Store2   350   340   403   210
# 2  Store3   250   180   145   160

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'],
                   'Class': [12, 12, 11],
                   'Marks': [88, 75, 93]})
print(df['Name'])
print(df[['Name', 'Marks']])

# Output:
# 0    Riya
# 1    Aman
# 2    Sara
# Name: Name, dtype: object
#    Name  Marks
# 0  Riya     88
# 1  Aman     75
# 2  Sara     93

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'], 'Marks': [88, 75, 93]},
                  index=['r1', 'r2', 'r3'])
print(df.loc['r2'])         # one row by label
print(df.iloc[0])           # first row by position
print(df.loc['r1':'r2'])    # rows r1 to r2 (r2 included)

# Output:
# Name     Aman
# Marks      75
# Name: r2, dtype: object
# Name     Riya
# Marks      88
# Name: r1, dtype: object
#     Name  Marks
# r1  Riya     88
# r2  Aman     75

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'], 'Marks': [88, 75, 93]})
print(df.loc[1, 'Name'])
print(df.at[2, 'Marks'])

# Output:
# Aman
# 93

import pandas as pd

result = pd.DataFrame({'Name': ['Atulya', 'Disha', 'Kavita', 'John'],
                       'Score': [12.5, 9.0, 16.5, 15.0],
                       'Attempts': [1, 3, 2, 1],
                       'Qualify': ['yes', 'no', 'yes', 'no']},
                      index=['a', 'b', 'c', 'd'])
print(result.loc[:, 'Attempts'] > 1)
print(result.tail(3))
print(result.loc[['a', 'd']])

# Output:
# a    False
# b     True
# c     True
# d    False
# Name: Attempts, dtype: bool
#      Name  Score  Attempts Qualify
# b   Disha    9.0         3      no
# c  Kavita   16.5         2     yes
# d    John   15.0         1      no
#      Name  Score  Attempts Qualify
# a  Atulya   12.5         1     yes
# d    John   15.0         1      no

import pandas as pd

data = [{'Name': 'Aarav', 'Age': 24}, {'Name': 'Priya', 'Age': 30},
        {'Name': 'Kunal', 'Age': 28}]
df = pd.DataFrame(data)
print(df['Age'] > 25)       # True / False for each row
print(df[df['Age'] > 25])   # only the rows where it is True

# Output:
# 0    False
# 1     True
# 2     True
# Name: Age, dtype: bool
#     Name  Age
# 1  Priya   30
# 2  Kunal   28

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara', 'Kabir'],
                   'Gender': ['F', 'M', 'F', 'M'],
                   'Marks': [88, 75, 93, 59]})
print(df[(df['Gender'] == 'F') & (df['Marks'] > 90)])
print(df[(df['Marks'] < 60) | (df['Marks'] > 90)])

# Output:
#    Name Gender  Marks
# 2  Sara      F     93
#     Name Gender  Marks
# 2   Sara      F     93
# 3  Kabir      M     59

import pandas as pd

Genre = pd.DataFrame({'Type': ['Fiction', 'Non Fiction', 'Drama', 'Poetry'],
                      'Code': ['F', 'NF', 'D', 'P']})
Genre['Num_Copies'] = [300, 290, 450, 760]
print(Genre)

# Output:
#           Type Code  Num_Copies
# 0      Fiction    F         300
# 1  Non Fiction   NF         290
# 2        Drama    D         450
# 3       Poetry    P         760

import pandas as pd

StoreDF = pd.DataFrame({'Items': ['Bread', 'Milk', 'Eggs'],
                        'Quantity': [50, 100, 120],
                        'Unit_Price': [30, 25, 6]})
StoreDF.loc[3] = ['Butter', 40, 55]
print(StoreDF)
print(StoreDF['Items'])

# Output:
#     Items  Quantity  Unit_Price
# 0   Bread        50          30
# 1    Milk       100          25
# 2    Eggs       120           6
# 3  Butter        40          55
# 0     Bread
# 1      Milk
# 2      Eggs
# 3    Butter
# Name: Items, dtype: object

import pandas as pd

Genre = pd.DataFrame({'Type': ['Fiction', 'Drama'], 'Code': ['F', 'D']})
Genre = Genre.rename(columns={'Code': 'Book_Code'})
print(Genre)

# Output:
#       Type Book_Code
# 0  Fiction         F
# 1    Drama         D

import pandas as pd

password = pd.DataFrame({
    'CodeName': ['aaaaaa', 'dragon', 'baseball', 'football', 'monkey', 'qwerty', 'abcde'],
    'Category': ['alpha', 'animal', 'sport', 'sport', 'animal', 'alpha', 'alpha'],
    'Frequency': [6.91, 18.52, 1.29, 11.11, 3.72, 1.85, 3.19]})

password.loc[len(password)] = ['abc123', 'alphanumeric', 12.8]   # (i) add a row
password = password.drop(2)                                     # (ii) delete row 2
password = password.drop('Frequency', axis=1)                   # (iii) delete the column
print(password)

# Output:
#    CodeName      Category
# 0    aaaaaa         alpha
# 1    dragon        animal
# 3  football         sport
# 4    monkey        animal
# 5    qwerty         alpha
# 6     abcde         alpha
# 7    abc123  alphanumeric

import pandas as pd

df = pd.DataFrame({'Name': ['Arpit', 'Vani', 'Chetan'],
                   'IP': [95, 89, 78], 'Maths': [88, 92, 80]})
df['Total'] = [183, 181, 158]                      # (i) add a column
df.rename(columns={'Name': 'Student'}, inplace=True)  # (ii) rename
df.drop(columns=['Maths'], inplace=True)          # (iii) delete a column
print(df)

# Output:
#   Student  IP  Total
# 0   Arpit  95    183
# 1    Vani  89    181
# 2  Chetan  78    158

import pandas as pd

dF = pd.DataFrame({'PRODUCT_ID': ['P001', 'P002', 'P003'],
                   'PRODUCT_NAME': ['Laptop', 'Mobile', 'Chair'],
                   'CATEGORY': ['Electronics', 'Electronics', 'Furniture'],
                   'PRICE': [60000, 20000, 3000]})
print(dF.iloc[0])                                  # (i) first row
print(dF['PRODUCT_NAME'])                          # (ii) one column
dF.loc[3] = ['P006', 'AC', 'HOME', 80000]          # (iii) add a row
dF = dF.drop('CATEGORY', axis=1)                   # (iv) delete a column
dF = dF.rename(columns={'PRODUCT_NAME': 'Item'})   # (v) rename a column
print(dF)

# Output:
# PRODUCT_ID             P001
# PRODUCT_NAME         Laptop
# CATEGORY        Electronics
# PRICE                 60000
# Name: 0, dtype: object
# 0    Laptop
# 1    Mobile
# 2     Chair
# Name: PRODUCT_NAME, dtype: object
#   PRODUCT_ID    Item  PRICE
# 0       P001  Laptop  60000
# 1       P002  Mobile  20000
# 2       P003   Chair   3000
# 3       P006      AC  80000

import pandas as pd

df = pd.DataFrame({'Store': ['Store1', 'Store2', 'Store3'],
                   'Qtr1': [300, 350, 250], 'Qtr2': [240, 340, 180],
                   'Qtr3': [450, 403, 145], 'Qtr4': [230, 210, 160]})
df['Total_Sales'] = df['Qtr1'] + df['Qtr2'] + df['Qtr3'] + df['Qtr4']
df = df.drop(2)            # delete the last row (label 2)
print(df)

# Output:
#     Store  Qtr1  Qtr2  Qtr3  Qtr4  Total_Sales
# 0  Store1   300   240   450   230         1220
# 1  Store2   350   340   403   210         1303

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'], 'Marks': [88, 75, 93]})
df = df.rename(index={0: 'a', 1: 'b'})
print(df)
df.index = ['x', 'y', 'z']
print(df)

# Output:
#    Name  Marks
# a  Riya     88
# b  Aman     75
# 2  Sara     93
#    Name  Marks
# x  Riya     88
# y  Aman     75
# z  Sara     93

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara'], 'Marks': [88, 75, 93]})
for label, row in df.iterrows():
    print(label, row['Name'], row['Marks'])

# Output:
# 0 Riya 88
# 1 Aman 75
# 2 Sara 93

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman'], 'Marks': [88, 75]})
for name, column in df.items():
    print("Column:", name)
    print(column.values)

# Output:
# Column: Name
# ['Riya' 'Aman']
# Column: Marks
# [88 75]

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Riya', 'Sara'],
                   'Marks': [88, 75, 88, 93]})
print(df.duplicated())
print(df.drop_duplicates())

# Output:
# 0    False
# 1    False
# 2     True
# 3    False
# dtype: bool
#    Name  Marks
# 0  Riya     88
# 1  Aman     75
# 3  Sara     93

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara', 'Kabir'],
                   'Marks': [88, 75, 93, 67]})
print(df.sort_values('Marks', ascending=False))

# Output:
#     Name  Marks
# 2   Sara     93
# 0   Riya     88
# 1   Aman     75
# 3  Kabir     67

import pandas as pd

sales = pd.DataFrame({
    'Category': ['Stationery', 'Grocery', 'Stationery', 'Grocery', 'Electronics'],
    'Item': ['Pen', 'Rice', 'Notebook', 'Oil', 'Earphones'],
    'Expenditure': [500, 2000, 800, 1500, 1200]})
print(sales.groupby('Category')['Expenditure'].sum())

# Output:
# Category
# Electronics    1200
# Grocery        3500
# Stationery     1300
# Name: Expenditure, dtype: int64

import pandas as pd

result = pd.DataFrame({'Name': ['Riya', 'Aman', 'Sara', 'Kabir'],
                       'English': [85, 72, 90, 64],
                       'Maths': [78, 95, 88, 70],
                       'IP': [92, 81, 97, 75]})
subjects = result[['English', 'Maths', 'IP']]
print("Average:")
print(subjects.mean())
print("Highest:")
print(subjects.max())
result['Total'] = subjects.sum(axis=1)       # axis=1: add across each row
print(result)
print("Topper:", result.loc[result['Total'].idxmax(), 'Name'])

# Output:
# Average:
# English    77.75
# Maths      82.75
# IP         86.25
# dtype: float64
# Highest:
# English    90
# Maths      95
# IP         97
# dtype: int64
#     Name  English  Maths  IP  Total
# 0   Riya       85     78  92    255
# 1   Aman       72     95  81    248
# 2   Sara       90     88  97    275
# 3  Kabir       64     70  75    209
# Topper: Sara

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman'], 'Marks': [88, 75]})
df.to_csv('marks.csv', index=False)
print(open('marks.csv').read())     # see what was written

# Output:
# Name,Marks
# Riya,88
# Aman,75

import pandas as pd

# make a small file to read
open('customer.csv', 'w').write("Name,Age,City\nRudra,17,Kolkata\nDivya,19,Dehradun\nNabeel,22,Lucknow\n")

DF = pd.read_csv('customer.csv')
print(DF)
print(DF.tail(2))     # the last two rows

# Output:
#      Name  Age      City
# 0   Rudra   17   Kolkata
# 1   Divya   19  Dehradun
# 2  Nabeel   22   Lucknow
#      Name  Age      City
# 1   Divya   19  Dehradun
# 2  Nabeel   22   Lucknow

import pandas as pd

open('data.csv', 'w').write("Item,Qty\nPen,10\nPencil,25\n")

df = pd.read_csv('data.csv')
print(df)

# Output:
#      Item  Qty
# 0     Pen   10
# 1  Pencil   25

import pandas as pd

df = pd.DataFrame({'Name': ['Riya', 'Aman'], 'City': ['Delhi', 'Pune']})
df.to_csv('people.csv', sep=';', index=False)
print(open('people.csv').read())

df2 = pd.read_csv('people.csv', sep=';')
print(df2)

# Output:
# Name;City
# Riya;Delhi
# Aman;Pune
#
#    Name   City
# 0  Riya  Delhi
# 1  Aman   Pune

import pandas as pd

open('scores.csv', 'w').write("Riya,88\nAman,75\nSara,93\n")

df = pd.read_csv('scores.csv', header=None, names=['Name', 'Marks'])
print(df)

# Output:
#    Name  Marks
# 0  Riya     88
# 1  Aman     75
# 2  Sara     93

import pandas as pd

open('students.csv', 'w').write("Name,Marks\nRiya,88\nAman,35\nSara,93\nKabir,28\n")

df = pd.read_csv('students.csv')
passed = df[df['Marks'] >= 40]
passed.to_csv('passed.csv', index=False)
print(open('passed.csv').read())

# Output:
# Name,Marks
# Riya,88
# Sara,93

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
temp = [30, 32, 31, 35, 33]
plt.plot(days, temp)
plt.show()

import matplotlib.pyplot as plt

months = ['April', 'May', 'June', 'July', 'August']
emission = [40, 50, 30, 60, 20]
plt.plot(months, emission, marker='o')
plt.title('Month wise CO2 emission')
plt.xlabel('Month')
plt.ylabel('Percentage')
plt.savefig('emission.png')     # save first ...
plt.show()                      # ... then show

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr']
sales = [12000, 15000, 11000, 18000]
plt.plot(months, sales, label="Sales")
plt.title("Sales Performance")
plt.xlabel("Month")
plt.ylabel("Amount")
plt.legend()
plt.show()

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 3, 5, 6]
plt.plot(x, y, color='red', linestyle='dashed', marker='s', linewidth=2)
plt.title('Styled Line')
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt

tests = ['Test 1', 'Test 2', 'Test 3', 'Test 4']
class_a = [65, 70, 78, 82]
class_b = [60, 74, 72, 85]
plt.plot(tests, class_a, marker='o', label='Class 12-A')
plt.plot(tests, class_b, marker='o', label='Class 12-B')
plt.title('Average Marks in Unit Tests')
plt.xlabel('Test')
plt.ylabel('Average Marks')
plt.legend()
plt.show()

import matplotlib.pyplot as plt

Fruits = ['Apple', 'Guava', 'Papaya', 'Grapes', 'Mango']
Price = [150, 70, 50, 30, 120]
plt.bar(Fruits, Price)
plt.title('Price of Fruits')
plt.xlabel('Fruits')
plt.ylabel('Price (Rs)')
plt.savefig('fruits.png')
plt.show()

import matplotlib.pyplot as plt

hobby = ['Dance', 'Music', 'Painting', 'Playing Sports']
people = [300, 400, 100, 500]
plt.bar(hobby, people)
plt.title('Favourite Hobby')
plt.xlabel('Hobbies')
plt.ylabel('Number of people')
plt.savefig('hobbies.png')
plt.show()

import matplotlib.pyplot as plt

subjects = ['English', 'Maths', 'IP', 'Economics']
marks = [85, 78, 95, 70]
plt.bar(subjects, marks, color=['red', 'green', 'blue', 'orange'], width=0.5)
plt.title('Marks of Riya')
plt.show()

plt.barh(subjects, marks, color='teal')
plt.title('Same Marks, Horizontal Bars')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

result = pd.DataFrame({'English': [85, 72, 90, 64],
                       'Maths': [78, 95, 88, 70],
                       'IP': [92, 81, 97, 75]})
avg = result.mean()
plt.bar(avg.index, avg.values, label='Average marks')
plt.title('Subject-wise Average Marks')
plt.xlabel('Subject')
plt.ylabel('Average')
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

subjects = ['English', 'Maths', 'IP']
boys = [72, 80, 85]
girls = [78, 76, 90]
x = np.arange(len(subjects))           # 0, 1, 2
plt.bar(x, boys, width=0.4, label='Boys')
plt.bar(x + 0.4, girls, width=0.4, label='Girls')
plt.xticks(x + 0.2, subjects)          # subject names under the pairs
plt.title('Average Marks: Boys vs Girls')
plt.legend()
plt.show()

import matplotlib.pyplot as plt

Height_cms = [145, 141, 142, 142, 143, 144, 141, 140, 143, 144]
plt.hist(Height_cms)
plt.title('Height of Students')
plt.xlabel('Height (cm)')
plt.ylabel('Number of students')
plt.savefig('heights.png')
plt.show()

import matplotlib.pyplot as plt

Ages = [22, 25, 24, 28, 30, 24, 29, 27, 21, 23]
binsize = [20, 22, 24, 26, 28, 30]
plt.hist(Ages, bins=binsize, edgecolor='black', label='Participants')
plt.xlabel('Age')
plt.ylabel('Number of Participants')
plt.title("Age Distribution")
plt.legend()
plt.show()

import matplotlib.pyplot as plt

marks = [35, 42, 58, 61, 67, 72, 75, 78, 81, 84, 88, 91, 95, 66, 54]
plt.hist(marks, bins=[33, 50, 60, 75, 90, 100], color='orange', edgecolor='black')
plt.title('Marks Distribution of Class 12')
plt.xlabel('Marks')
plt.ylabel('Number of students')
plt.show()