31/01/2024

Practical → 1st (a)

Q1 Write programs in Python using NumPy library to do the following:
A. Compute the mean, standard deviation, and variance of a two-dimensional
random integer array along the second axis.
CODE:
import numpy as np
x=np.arange(6)
print("\nOriginal array:")
print(x)
r1=np.mean(x)
r2=np.average(x)
print("\nMean:",r1)
r1=np.std(x)
r2=np.sqrt(np.mean((x-np.mean(x))**2))
print("\nstd:",r1)
r1=np.var(x)
r2=np.mean((x-np.mean(x))**2)
print("\nvariance;",r1)

OUTPUT:
01/02/2024

Practical → 1st (b)

Q1 Write programs in Python using NumPy library to do the following:
B. Create a 2-dimensional array of size m x n integer elements, also print the shape,
type and data type of the array and then reshape it into an n x m array, where n
and m are user inputs given at the run time
CODE:
import numpy as np
x = np.array([[2, 4, 6], [6, 8, 10]])
print("First Array : ")
print(x)
print("Type of Array")
print(type(x))
print("Shape of Array")
print(x.shape)
print(x.dtype)
reshaped2 = np.reshape(x, (3, 2))
print("Second Reshaped Array : ")
print(reshaped2)

OUTPUT:
21/02/2024

Practical → 1st (c)

Q1 Write programs in Python using NumPy library to do the following:
C. Test whether the elements of a given 1D array are zero, non-zero, and NaN.
Record the indices of these elements in three separate arrays.
CODE:
import numpy as np
arr = np.array([0, 1, 2, 0, np.nan, 5, 0])
zero_indices = np.where(arr == 0)[0]
non_zero_indices = np.where(arr != 0)[0]
nan_indices = np.where(np.isnan(arr))
print("Zero indices:", zero_indices)
print("Non-zero indices:", non_zero_indices)
print("NaN indices:", nan_indices)

OUTPUT:
21/02/2024

Practical → 1st (d)

Q1 Write programs in Python using NumPy library to do the following:
D. Create three random arrays of the same size: Array1, Array2, and
Array3. Subtract Array 2 from Array3 and store in Array4. Create
another array Array5 having two times the values in Array1. Find
Covariance and Correlation of Array1 with Array4 and Array5
respectively
CODE:
import numpy as np

# Create three random arrays of the same size

size = 100
Array1 = np.random.rand(size)
Array2 = np.random.rand(size)
Array3 = np.random.rand(size)

# Subtract Array2 from Array3 and store in Array4

Array4 = Array3 - Array2

# Create Array5 having two times the values in Array1

Array5 = 2 * Array1

# Find Covariance of Array1 with Array4

covariance_1_4 = np.cov(Array1, Array4)[0][1]

# Find Correlation of Array1 with Array5

correlation_1_5 = np.corrcoef(Array1, Array5)[0][1]

print("Covariance of Array1 with Array4:", covariance_1_4)

print("Correlation of Array1 with Array5:", correlation_1_5)

OUTPUT:
21/02/2024

Practical → 1st (e)

Q1 Write programs in Python using NumPy library to do the following:
E. Create two random arrays of the same size 10: Array1, and Array2. Find the sum
of the first half of both the arrays and the product of the second half of both
arrays.
CODE:
import numpy as np

# Create two random arrays of the same size 10

size = 10
Array1 = np.random.rand(size)
Array2 = np.random.rand(size)

# Find the sum of the first half of both arrays

sum_first_half_Array1 = np.sum(Array1[:size//2])
sum_first_half_Array2 = np.sum(Array2[:size//2])

# Find the product of the second half of both arrays

product_second_half_Array1 = np.prod(Array1[size//2:])
product_second_half_Array2 = np.prod(Array2[size//2:])

print("Sum of the first half of Array1:", sum_first_half_Array1)

print("Sum of the first half of Array2:", sum_first_half_Array2)
print("Product of the second half of Array1:", product_second_half_Array1)
print("Product of the second half of Array2:", product_second_half_Array2)

OUTPUT:
28/02/2024

Practical → 2nd (a)

Q2 Do the following using PANDAS Series:
A. Create a series with 5 elements. Display the series sorted on index and also sorted
on values separately.
CODE:
import pandas as pd

# Create a series with 5 elements

series = pd.Series([10, 5, 8, 2, 7], index=['e', 'a', 'd', 'c', 'b'])

# Display the series sorted on index

sorted_by_index = series.sort_index()

# Display the series sorted on values

sorted_by_values = series.sort_values()

print("Series sorted on index:")

print(sorted_by_index)

print("\nSeries sorted on values:")

print(sorted_by_values)

OUTPUT:
28/02/2024

Practical → 2nd (b)

Q2 Do the following using PANDAS Series:
B. Create a series with N elements with some duplicate values. Find the minimum
and maximum ranks assigned to the values using the ‘first’ and ‘max’ methods.
CODE:
import pandas as pd
series = pd.Series([2, 4, 6, 2, 8, 6, 3, 7, 4, 5])
min_ranks_first = series.rank(method='first')
max_ranks_max = series.rank(method='max')

print("Series:")
print(series)
print("\nMinimum ranks (using 'first' method):")
print(min_ranks_first)
print("\nMaximum ranks (using 'max' method):")
print(max_ranks_max)

OUTPUT:
28/02/2024

Practical → 2nd (c)

Q2 Do the following using PANDAS Series:
C. Display the index value of the minimum and maximum element of a
Series.
CODE:
import pandas as pd

# Create a sample series

series = pd.Series([10, 5, 8, 2, 7])

# Find index value of the minimum element

min_index = series.idxmin()

# Find index value of the maximum element

max_index = series.idxmax()

print("Index value of the minimum element:", min_index)

print("Index value of the maximum element:", max_index)

OUTPUT:
06/03/2024

Practical → 3rd (a)

Q3 Create a data frame having at least 3 columns and 50 rows to store numeric data
generated using a random function. Replace 10% of the values by null values whose
index positions are generated using random function. Do the following:
a. Identify and count missing values in a data frame.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
missing_values_count = df.isnull().sum()
print("Missing values count:")
print(missing_values_count)

OUTPUT:
06/03/2024

Practical → 3rd (b)

b. Drop the column having more than 5 null values.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
df=df.dropna(axis=1, thresh=45)
print(df)

OUTPUT:
06/03/2024

Practical → 3rd (c)

c. Identify the row label having maximum of the sum of all values in a row and drop that
row.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
row_sums = df.sum(axis=1)
max_row_label = row_sums.idxmax()
df = df.drop(index=max_row_label)
print(df)

OUTPUT:
06/03/2024

Practical → 3rd (d)

d. Sort the data frame on the basis of the first column.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
df_sorted = df.sort_values(by='Column1')
print(df_sorted)

OUTPUT:
06/03/2024

Practical → 3rd (e)

e. Remove all duplicates from the first column.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
df_unique=df.drop_duplicates(subset=['Column1'])
print(df_unique)

OUTPUT:
06/03/2024

Practical → 3rd (f)

f. Find the correlation between first and second column and covariance between second
and third column.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
# Calculate correlation between first and second column
correlation_first_second = df['Column1'].corr(df['Column2'])

# Calculate covariance between second and third column

covariance_second_third = df['Column2'].cov(df['Column3'])

print("Correlation between first and second column:", correlation_first_second)

print("Covariance between second and third column:", covariance_second_third)

OUTPUT:
06/03/2024

Practical → 3rd (g)

g. Discretize the second column and create 5 bins.
CODE:
import pandas as pd
import numpy as np
data = np.random.randn(50, 3)
df = pd.DataFrame(data, columns=['Column1', 'Column2', 'Column3'])
null_indices = np.random.choice(df.index, size=int(0.1 * len(df)), replace=False)
for idx in null_indices:
col_idx = np.random.randint(0, 3)
df.iloc[idx, col_idx] = np.nan
df['Column2_bins'] = pd.cut(df['Column2'], bins=5)
print(df)

OUTPUT:
13/03/2024

Practical → 6th
Q Consider the following data frame containing a family name, gender of the family
member and her/his monthly income in each record.

CODE:
import pandas as pd

# Creating the DataFrame

data = {
'Name': ['Shah', 'Vats', 'Vats', 'Kumar', 'Vats', 'Kumar', 'Shah', 'Shah', 'Kumar',
'Vats'],
'Gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male', 'Female',
'Female', 'Male'],
'MonthlyIncome': [114000.00, 65000.00, 43150.00, 69500.00, 155000.00,
103000.00, 55000.00, 112400.00, 81030.00, 71900.00]
}

df = pd.DataFrame(data)
print(df)
OUTPUT:
13/03/2024

Practical → 6th (a)

Q Write a program in Python using Pandas to perform the following:
a. Calculate and display familywise gross monthly income.
CODE:
import pandas as pd
data = {
'Name': ['Shah', 'Vats', 'Vats', 'Kumar', 'Vats', 'Kumar', 'Shah', 'Shah', 'Kumar',
'Vats'],
'Gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male', 'Female',
'Female', 'Male'],
'MonthlyIncome': [114000.00, 65000.00, 43150.00, 69500.00, 155000.00,
103000.00, 55000.00, 112400.00, 81030.00, 71900.00]
}
df = pd.DataFrame(data)
family_income = df.groupby('Name')['MonthlyIncome'].sum()
print("Familywise gross monthly income:")
print(family_income)
print()
OUTPUT:
13/03/2024

Practical → 6th (b)

Q Write a program in Python using Pandas to perform the following:
b. Calculate and display the member with the highest monthly income.
CODE:
import pandas as pd
data = {
'Name': ['Shah', 'Vats', 'Vats', 'Kumar', 'Vats', 'Kumar', 'Shah', 'Shah', 'Kumar',
'Vats'],
'Gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male', 'Female',
'Female', 'Male'],
'MonthlyIncome': [114000.00, 65000.00, 43150.00, 69500.00, 155000.00,
103000.00, 55000.00, 112400.00, 81030.00, 71900.00]
}
df = pd.DataFrame(data)
highest_income_member = df.loc[df['MonthlyIncome'].idxmax()]
print("Member with the highest monthly income:")
print(highest_income_member)
print()
OUTPUT:
13/03/2024

Practical → 6th (c)

Q Write a program in Python using Pandas to perform the following:
c. Calculate and display monthly income of all members with income greater than Rs.
60000.00.
CODE:
import pandas as pd
data = {
'Name': ['Shah', 'Vats', 'Vats', 'Kumar', 'Vats', 'Kumar', 'Shah', 'Shah', 'Kumar',
'Vats'],
'Gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male', 'Female',
'Female', 'Male'],
'MonthlyIncome': [114000.00, 65000.00, 43150.00, 69500.00, 155000.00,
103000.00, 55000.00, 112400.00, 81030.00, 71900.00]
}
df = pd.DataFrame(data)
high_income_members = df[df['MonthlyIncome'] > 60000.00]
print("Monthly income of members with income greater than Rs. 60000.00:")
print(high_income_members[['Name', 'MonthlyIncome']])
print()
OUTPUT:
13/03/2024

Practical → 6th (d)

Q Write a program in Python using Pandas to perform the following:
d. Calculate and display the average monthly income of the female members
CODE:
import pandas as pd
data = {
'Name': ['Shah', 'Vats', 'Vats', 'Kumar', 'Vats', 'Kumar', 'Shah', 'Shah', 'Kumar',
'Vats'],
'Gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Male', 'Male', 'Female',
'Female', 'Male'],
'MonthlyIncome': [114000.00, 65000.00, 43150.00, 69500.00, 155000.00,
103000.00, 55000.00, 112400.00, 81030.00, 71900.00]
}
df = pd.DataFrame(data)
female_avg_income = df[df['Gender'] == 'Female']['MonthlyIncome'].mean()
print("Average monthly income of female members:", female_avg_income)

OUTPUT:
21/03/2024

Practical → 7th (a)

Q7. Using Titanic dataset, to do the following:
A. Find the total number of passengers with age less than 30.

CODE:
import pandas as pd
titanic_df = pd.read_csv("C:/Users/DELL/Downloads/titanic.csv")
# a. Total number of passengers with age less than 30
passengers_under_30 = titanic_df[titanic_df['Age'] < 30]
total_passengers_under_30 = passengers_under_30.shape[0]
print("Total number of passengers with age less than 30:",
total_passengers_under_30)

OUTPUT:
21/03/2024

Practical → 7th (b)

Q7. Using Titanic dataset, to do the following:
B. Find total fare paid by passengers of first class.

CODE:
import pandas as pd
titanic_df = pd.read_csv("C:/Users/DELL/Downloads/titanic.csv")
# b. Total fare paid by passengers of first class
first_class_fare = titanic_df[titanic_df['Pclass'] == 1]['Fare'].sum()
print("Total fare paid by passengers of first class:", first_class_fare)

OUTPUT:
21/03/2024

Practical → 7th (c)

Q7. Using Titanic dataset, to do the following:
C. Compare number of survivors of each passenger class

CODE:
import pandas as pd
titanic_df = pd.read_csv("C:/Users/DELL/Downloads/titanic.csv")
# c. Number of survivors of each passenger class
survivors_by_class = titanic_df.groupby('Pclass')['Survived'].sum()
print("Number of survivors of each passenger class:")
print(survivors_by_class)

OUTPUT:
21/03/2024

Practical → 7th (d)

Q7. Using Titanic dataset, to do the following:
D. Compute descriptive statistics for any numeric attribute genderwise

CODE:
import pandas as pd
titanic_df = pd.read_csv("C:/Users/DELL/Downloads/titanic.csv")
# d. Descriptive statistics for age attribute genderwise
descriptive_stats_genderwise = titanic_df.groupby('Sex')['Age'].describe()
print("Descriptive statistics for age attribute genderwise:")
print(descriptive_stats_genderwise)

OUTPUT:
17/04/2024

Practical → 4th
Q4. Consider two Excel files having an attendance of two workshops. Each file has three
fields ‘Name’, ‘Date, duration (in minutes) where names are unique within a file. Note
that duration may take one of three values (30, 40, 50) only. Import the data into two
data frames.
CODE:
import pandas as pd

# Create dummy data for workshop1

workshop1_data = {
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Date': ['2024-04-01', '2024-04-02', '2024-04-03', '2024-04-04'],
'Duration': [30, 40, 50, 30]
}

# Create dummy data for workshop2

workshop2_data = {
'Name': ['Alice', 'Eve', 'Charlie', 'Frank'],
'Date': ['2024-04-03', '2024-04-04', '2024-04-05', '2024-04-06'],
'Duration': [30, 40, 50, 30]
}

# Create data frames from the dummy data

df1 = pd.DataFrame(workshop1_data)
df2 = pd.DataFrame(workshop2_data)

# Display the first few rows of each data frame to verify the data
print("Data Frame 1:")
print(df1)

print("\nData Frame 2:")

print(df2)

OUTPUT:
Q. Import the data into two data frames and do the following:
a. Perform a merging of the two data frames to find the names of students
who had attended both workshops.
b. Find the names of all students who have attended a single workshop only.
c. Merge two data frames row-wise and find the total number of records in
the data frame.
d. Merge two data frames row-wise and use two columns viz. names and
dates as multi-row indexes. Generate descriptive statistics for this
hierarchical data frame.
CODE:
# a. Perform merging of the two data frames to find the names of students who had attended
both workshops.
attended_both = pd.merge(df1, df2, how='inner', on='Name')
print("\nNames of students who attended both workshops:")
print(attended_both['Name'].unique())

# b. Find names of all students who have attended a single workshop only.
attended_either = pd.merge(df1, df2, how='outer', on='Name', indicator=True)
attended_single = attended_either[attended_either['_merge'].isin(['left_only', 'right_only'])]
print("\nNames of students who attended a single workshop only:")
print(attended_single['Name'].unique())

# c. Merge two data frames row-wise and find the total number of records in the data frame.
merged_df = pd.concat([df1, df2], ignore_index=True)
print("\nTotal number of records in the merged data frame:", len(merged_df))

# d. Merge two data frames row-wise and use two columns viz. names and dates as multi-row
indexes.
# Generate descriptive statistics for this hierarchical data frame.
merged_df_multi_index = pd.concat([df1.set_index(['Name', 'Date']), df2.set_index(['Name',
'Date'])], axis=0)
print("\nDescriptive statistics for the hierarchical data frame:")
print(merged_df_multi_index.describe())
OUTPUT:
24/04/2024

Practical → 5th (a)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
a. Plot bar chart to show the frequency of each class label in the data.
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# a. Plot bar chart to show the frequency of each class label in the data.
plt.figure(figsize=(8, 6))
sns.countplot(x='species', data=iris_df)
plt.title('Frequency of each class label')
plt.xlabel('Species')
plt.ylabel('Frequency')
plt.show()

OUTPUT:
24/04/2024

Practical → 5th (b)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
b. Draw a scatter plot for Petal width vs sepal width and fit a regression line .
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# b. Draw a scatter plot for Petal width vs sepal width and fit a regression line
plt.figure(figsize=(8, 6))
sns.regplot(x='petal_width', y='sepal_width', data=iris_df)
plt.title('Petal width vs Sepal width')
plt.xlabel('Petal width (cm)')
plt.ylabel('Sepal width (cm)')
plt.show()

OUTPUT:
24/04/2024

Practical → 5th (c)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
c. Plot density distribution for feature petal length.
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# c. Plot density distribution for feature petal length.
plt.figure(figsize=(8, 6))
sns.kdeplot(iris_df['petal_length'], shade=True)
plt.title('Density distribution of Petal length')
plt.xlabel('Petal length (cm)')
plt.ylabel('Density')
plt.show()

OUTPUT:
24/04/2024

Practical → 5th (d)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
d. Use a pair plot to show pairwise bivariate distribution in the Iris Dataset.
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# d. Use a pair plot to show pairwise bivariate distribution in the Iris Dataset.
plt.figure(figsize=(10, 8))
sns.pairplot(iris_df, hue='species')
plt.show()

OUTPUT:
24/04/2024

Practical → 5th (e)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
e. Draw heatmap for the four numeric attributes.
CODE:
mport pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# Drop the 'species' column
numeric_df = iris_df.drop(columns='species')
# Draw heatmap for the four numeric attributes
plt.figure(figsize=(8, 6))
sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Heatmap for numeric attributes')
plt.show()

OUTPUT:
24/04/2024

Practical → 5th (f)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
f. Compute mean, mode, median, standard deviation, confidence interval and
standard error for each feature
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# Compute statistics for numeric features
numeric_stats = iris_df.describe().transpose()
# Compute mode for non-numeric features separately
non_numeric_modes =
iris_df.select_dtypes(include=['object']).mode().transpose().iloc[:, 0]
# Compute standard error
standard_error = iris_df.select_dtypes(include=['number']).sem().values
# Compute 95% confidence interval
n = iris_df.shape[0]
confidence_interval = 1.96 * iris_df.select_dtypes(include=['number']).std() / (n ** 0.5)
# Combine statistics
feature_stats = pd.concat([numeric_stats, pd.DataFrame(non_numeric_modes,
columns=['mode']),
pd.DataFrame(standard_error, columns=['standard error']),
pd.DataFrame((numeric_stats['mean'] - confidence_interval).values,
columns=['95% CI (low)']),
pd.DataFrame((numeric_stats['mean'] + confidence_interval).values,
columns=['95% CI (high)'])], axis=1)
print("\nFeature statistics:")
print(feature_stats)

OUTPUT:
24/04/2024

Practical → 5th (g)

Q5. Using Iris data, plot the following with proper legend and axis labels: (Download
IRIS data from: https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn
datasets)
g. Compute correlation coefficients between each pair of features and plot heatmap.
CODE:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load Iris dataset
iris_df = sns.load_dataset('iris')
# Exclude non-numeric column 'species'
numeric_columns = iris_df.select_dtypes(include=[float, int]).columns
iris_numeric_df = iris_df[numeric_columns]
# Compute correlation coefficients between each pair of features and plot heatmap
correlation_matrix = iris_numeric_df.corr()
plt.figure(figsize=(8, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation heatmap')
plt.show()
OUTPUT: