DAVIET/CSE/2003635
D.A.V INSTITUTE OF ENGINERING & TECHNOLOGY
JALANDHAR
Data Science LAB(BTCS 616-18)
Submitted To:- Submitted By:-
Dr. Vinay Chopra Amandeep
(Associate Professor) Roll:-105/20
Branch :- CSE/6 th Sem
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INDEX
S.NO. EXPERIMENTS Page Remarks
No.
1 Using the pandas Python Library 3-4
2 a) Write a basic code in python for Matrix input from user 5-6
b) Create Matrix using map function using Numpy
c) Write a python program to demonstrate the operations
add , subtract and divide using Numpy
3 Combine two Dataframe in Python-Pandas 7-9
a) Concatenating dataframe
b) Joining dataframe
c) Concatenating using append
4 Adding new column to existing dataframe in Pandas 10-12
a) By declaring a new list as a column.
b) By using dataframe insert()
c) Using dataframe assign() method
d) By using a dictionary
5 Create a new column in Pandas dataframe based on the 13-15
existing columns
a)Use dataframe. Apply()
b) We can achieve the same result by directly perfroming
the required operation on the desired column element-wise
c) Using dataframe .map() function
6 Create a new column in Pandas dataframe based on a given . 16-17
condition
a) Using list comprehension
b)Using dataframe.apply() function
c)Using dataframe .map() function
d) Using numpy.where() function
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7 Selecting row in Pandas dataframe based on a given condition 18-20
a) Selecting all the rows from the gives dataframes in which
‘percentage’ is greater than 80% using loc()
b) Selecting those rows whose column value is present in the
list using isin() method of the dataframe
c) Selecting rows based on multiple column conditions using
’&’ operator
8 Pandas dataframe where() 21
9 Create Box Plot. 22
10 Create Histogram 23
11 Create Pivot Table. 24
12 Create Heapmap. 25
13 Demostrating means() in Pyhton program 26
14 Demostrating standard deviation in python program 27
15 Calculation skewness and kurtosis in python 28
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Task 1: Using the pandas Python Library
import requests
download_url =
"https://raw.githubusercontent.com/fivethirtyeight/data/master/nba-elo/
nbaallelo.csv"
target_csv_path = "nba_all_elo.csv"
response = requests.get(download_url)
response.raise_for_status() # Check that the request was successful
with open(target_csv_path, "wb") as f:
f.write(response.content)
print("Download ready.")
import pandas as pd
nba = pd.read_csv("nba_all_elo.csv")
type(nba)
len(nba)
nba.shape
nba.describe()
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import numpy as np
nba.describe(include=object
nba["team_id"].value_counts()
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Task 2: a) Write a basic code in python for Matrix input from user
INPUT:
R = int(input("Enter the number of rows:"))
C = int(input("Enter the number of columns:"))
matrix = []
print("Enter the entries rowwise:")
for i in range(R): # A for loop for row entries
a =[]
for j in range(C): # A for loop for column entries
a.append(int(input()))
matrix.append(a)
for i in range(R):
for j in range(C):
print(matrix[i][j], end = " ")
print()
OUTPUT:
b) Create Matrix using map function using Numpy
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INPUT:
import numpy as np
R = int(input("Enter the number of rows:"))
C = int(input("Enter the number of columns:"))
print("Enter the entries in a single line (separated by space): ")
entries = list(map(int, input().split()))
matrix = np.array(entries).reshape(R, C)
print(matrix)
OUTPUT:
c) Write a python program to demonstrate the operations add , subtract and divide
using Numpy
INPUT:
import numpy as np
print("Add:")
print(np.add(1.0, 4.0))
print("Subtract:")
print(np.subtract(1.0, 4.0))
print("Multiply:")
print(np.multiply(1.0, 4.0))
print("Divide:")
print(np.divide(1.0, 4.0))
OUTPUT:
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Task 3: Combine two Dataframe in Python-Pandas
a) Concatenating dataframe
INPUT:
import pandas as pd
df1 = pd.DataFrame({'id': ['A01', 'A02', 'A03', 'A04'],
'Name': ['ABC', 'PQR', 'DEF', 'GHI']})
df2 = pd.DataFrame({'id': ['B05', 'B06', 'B07', 'B08'],
'Name': ['XYZ', 'TUV', 'MNO', 'JKL']})
frames = [df1, df2]
result = pd.concat(frames)
display(result)
OUTPUT:
b) Joining dataframe
INPUT:
import pandas as pd
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df1 = pd.DataFrame({'id': ['A01', 'A02', 'A03', 'A04'],
'Name': ['ABC', 'PQR', 'DEF', 'GHI']})
df3 = pd.DataFrame({'City': ['MUMBAI', 'PUNE', 'MUMBAI', 'DELHI'],
'Age': ['12', '13', '14', '12']})
result = pd.concat([df1, df3], axis=1, join='inner')
display(result)
OUTPUT:
c) Concatenating using append
INPUT:
import pandas as pd
df1 = pd.DataFrame({'id': ['A01', 'A02', 'A03', 'A04'],
'Name': ['ABC', 'PQR', 'DEF', 'GHI']})
df2 = pd.DataFrame({'id': ['B05', 'B06', 'B07', 'B08'],
'Name': ['XYZ', 'TUV', 'MNO', 'JKL']})
result = df1.append(df2)
display(result)
OUTPUT:
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Task 4: Adding new column to existing dataframe in Pandas
a) By declaring a new list as a column.
INPUT:
import pandas as pd
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Height': [5.1, 6.2, 5.1, 5.2],
'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}
df = pd.DataFrame(data)
address = ['Delhi', 'Bangalore', 'Chennai', 'Patna']
df['Address'] = address
print(df)
OUTPUT:
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b) By using dataframe insert()
INPUT:
import pandas as pd
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Height': [5.1, 6.2, 5.1, 5.2],
'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}
df = pd.DataFrame(data)
df.insert(2, "Age", [21, 23, 24, 21], True)
print(df)
OUTPUT:
c) Using dataframe assign() method
INPUT:
import pandas as pd
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Height': [5.1, 6.2, 5.1, 5.2],
'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}
df = pd.DataFrame(data)
df2 = df.assign(address=['Delhi', 'Bangalore', 'Chennai', 'Patna'])
print(df2)
OUTPUT:
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d) By using a dictionary
INPUT:
import pandas as pd
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Height': [5.1, 6.2, 5.1, 5.2],
'Qualification': ['Msc', 'MA', 'Msc', 'Msc']}
address = {'Delhi': 'Jai', 'Bangalore': 'Princi',
'Patna': 'Gaurav', 'Chennai': 'Anuj'}
df = pd.DataFrame(data)
df['Address'] = address
print(df)
OUTPUT:
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Task 5: Create a new column in Pandas dataframe based on the existing columns
a) Use dataframe. Apply()
INPUT:
import pandas as pd
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
'Event':['Music', 'Poetry', 'Theatre', 'Comedy'],
'Cost':[10000, 5000, 15000, 2000]})
print(df)
OUTPUT:
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b) We can achieve the same result by directly perfroming the required operation on
the desired column element-wise
INPUT:
import pandas as pd
df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
'Event':['Music', 'Poetry', 'Theatre', 'Comedy'],
'Cost':[10000, 5000, 15000, 2000]})
df['Discounted_Price'] = df['Cost'] - (0.1 * df['Cost'])
print(df)
df['Discounted_Price'] = df.apply(lambda row: row.Cost -
(row.Cost * 0.1), axis = 1)
print(df)
OUTPUT:
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c) Using dataframe .map() function
INPUT:
data = {
"name": ["John", "Ted", "Dev", "Brad", "Rex", "Smith", "Samuel", "David"],
"salary": [10000, 20000, 50000, 45500, 19800, 95000, 5000, 50000]
}
df = pd.DataFrame(data)
display(df.head())
def salary_stats(value):
if value < 10000:
return "very low"
if 10000 <= value < 25000:
return "low"
elif 25000 <= value < 40000:
return "average"
elif 40000 <= value < 50000:
return "better"
elif value >= 50000:
return "very good"
df['salary_stats'] = df['salary'].map(salary_stats)
display(df.head())
OUTPUT:
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Task 6: Create a new column in Pandas dataframe based on a given condition
a) Using list comprehension
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INPUT:
import pandas as pd
df = pd.DataFrame({'Date' : ['11/8/2011', '11/9/2011', '11/10/2011',
'11/11/2011', '11/12/2011'],
'Event' : ['Music', 'Poetry', 'Music', 'Comedy', 'Poetry']})
print(df)
df['Price'] = [1500 if x =='Music' else 800 for x in df['Event']]
print(df)
OUTPUT:
b) Using dataframe.apply() function
INPUT:
def set_value(row_number, assigned_value):
return assigned_value[row_number]
event_dictionary ={'Music' : 1500, 'Poetry' : 800, 'Comedy' : 1200}
df['Price'] = df['Event'].apply(set_value, args =(event_dictionary, ))
print(df)
OUTPUT:
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c) Using dataframe .map() function
INPUT:
event_dictionary ={'Music' : 1500, 'Poetry' : 800, 'Comedy' : 1200}
df['Price'] = df['Event'].map(event_dictionary)
print(df)
OUTPUT:
d) Using numpy.where() function
INPUT:
df['Price'] = np.where(df['Event']
=='Music', 1500,800 )
print(df)
OUTPUT:
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Task 7: Selecting row in Pandas dataframe based on a given condition
a) Selecting all the rows from the gives dataframes in which ‘percentage’ is greater
than 80% using loc()
INPUT:
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78] }
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream',
'Percentage'])
print("Given Dataframe :\n", dataframe)
rslt_df = dataframe[dataframe['Percentage'] > 80]
print('\nResult dataframe :\n', rslt_df)
OUTPUT:
b) Selecting those rows whose column value is present in the list using isin()
method of the dataframe
INPUT:
import pandas as pd
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record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream',
'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Commerce']
rslt_df = dataframe[dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
OUTPUT:
c) Selecting rows based on multiple column conditions using ’&’ operator
INPUT:
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
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dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream',
'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Science']
rslt_df = dataframe[(dataframe['Age'] == 21) &
dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
OUTPUT:
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Task 8: Pandas dataframe where()
INPUT:
import pandas as pd
data = {
"age": [50, 40, 30, 40, 20, 10, 30],
"qualified": [True, False, False, False, False, True, True]
}
df = pd.DataFrame(data)
print(df)
newdf = df.where(df["age"] > 30)
print(newdf)
OUTPUT:
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Task 9: Create Box Plot
INPUT:
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(10)
data_1 = np.random.normal(100, 10, 200)
data_2 = np.random.normal(90, 20, 200)
data_3 = np.random.normal(80, 30, 200)
data_4 = np.random.normal(70, 40, 200)
data = [data_1, data_2, data_3, data_4]
fig = plt.figure(figsize =(10, 7))
ax = fig.add_axes([0, 0, 1, 1])
bp = ax.boxplot(data)
plt.show()
OUTPUT:
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Task 10: Create Histogram
INPUT:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
np.random.seed(23685752)
N_points = 10000
n_bins = 20
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
fig, axs = plt.subplots(1, 1,
figsize =(6, 5),
tight_layout = True)
axs.hist(x, bins = n_bins)
plt.show()
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OUTPUT:
Task 11: Create Pivot Table
INPUT:
import pandas as pd
df = pd.DataFrame({'Product' : ['Carrots', 'Broccoli', 'Banana', 'Banana',
'Beans', 'Orange', 'Broccoli', 'Banana'],
'Category' : ['Vegetable', 'Vegetable', 'Fruit', 'Fruit',
'Vegetable', 'Fruit', 'Vegetable', 'Fruit'],
'Quantity' : [8, 5, 3, 4, 5, 9, 11, 8],
'Amount' : [270, 239, 617, 384, 626, 610, 62, 90]})
pivot = df.pivot_table(index =['Product'],
values =['Amount'],
aggfunc ='sum')
print(pivot)
OUTPUT:
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Task 12: Create Heapmap
INPUT:
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
data_set = np.random.rand( 10 , 10 )
ax = sns.heatmap( data_set , linewidth = 0.5 , cmap = 'coolwarm' )
plt.title( "2-D Heat Map" )
plt.show()
OUTPUT:
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Task 13: Demostrating means() in Pyhton program
INPUT:
import statistics
data1 = [1, 3, 4, 5, 7, 9, 2]
x = statistics.mean(data1)
print("Mean is :", x)
OUTPUT:
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Task 14: Demostrating standard deviation in python program
INPUT:
import statistics
sample = [1, 2, 3, 4, 5]
print("Standard Deviation of sample is % s " % (statistics.stdev(sample)))
OUTPUT:
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Task 15: Calculation skewness and kurtosis in python
INPUT:
import scipy
from scipy.stats import kurtosis
dataset = [10, 25, 14, 26, 35, 45, 67, 90,
40, 50, 60, 10, 16, 18, 20]
from scipy.stats import skew
dataset = [88, 85, 82, 97, 67, 77, 74, 86,
81, 95, 77, 88, 85, 76, 81]
print("SKEWNESS")
print(skew(dataset, axis=0, bias=True))
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print("KURTOSIS")
OUTPUT:
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