Why Python Is Required for Data Analyst

1# Writing to a file
2try:
3    with open("example.txt", "w") as file:  # "w" mode means write (creates or truncates)
4        file.write("Hello, world!\n")
5        file.write("This is an example of file handling in Python.")
6except IOError as e:
7    print("Error writing to file:", e)
8
9# Reading from a file
10try:
11    with open("example.txt", "r") as file:  # "r" mode means read
12        content = file.read()
13        print("File content:\n", content)
14except IOError as e:
15    print("Error reading from file:", e)
16

1try:
2    # Code that may raise an exception
3    numerator = 10
4    denominator = 0
5    result = numerator / denominator
6    print("The result is:", result)
7except ZeroDivisionError as e:
8    # Handle the specific exception
9    print("Error: Cannot divide by zero.")
10    print("Exception message:", e)
11except Exception as e:
12    # Handle any other exceptions
13    print("An unexpected error occurred:", e)
14else:
15    # Code that runs if no exception occurred
16    print("Division was successful.")
17finally:
18    # Code that always runs, regardless of an exception
19    print("Execution of the try-except block is complete.")
20

1pip --version
2

1pip install numpy

1import numpy as np

1import numpy as np
2
3# Create a 1D array
4array_1d = np.array([1, 2, 3, 4, 5])
5print("1D Array:", array_1d)
6
7# Create a 2D array
8array_2d = np.array([[1, 2, 3], [4, 5, 6]])
9print("2D Array:\n", array_2d)
10
11# Element-wise addition
12added_array = array_1d + 10
13print("Added Array:", added_array)
14
15# Element-wise multiplication
16multiplied_array = array_1d * 2
17print("Multiplied Array:", multiplied_array)
18
19# Accessing elements
20first_element = array_1d[0]
21print("First Element:", first_element)
22
23# Slicing arrays
24slice_array = array_1d[1:4]
25print("Sliced Array:", slice_array)
26
27# Generate an array of zeros
28zeros_array = np.zeros((3, 3))
29print("Zeros Array:\n", zeros_array)
30
31# Generate an array of ones
32ones_array = np.ones((2, 4))
33print("Ones Array:\n", ones_array)
34
35# Generate an identity matrix
36identity_matrix = np.eye(3)
37print("Identity Matrix:\n", identity_matrix)
38
39# Calculate the mean of an array
40mean_value = np.mean(array_1d)
41print("Mean Value:", mean_value)
42
43# Calculate the sum of elements in an array
44sum_value = np.sum(array_1d)
45print("Sum Value:", sum_value)
46
47# Calculate the standard deviation of an array
48std_dev = np.std(array_1d)
49print("Standard Deviation:", std_dev)
50
51# Matrix multiplication
52matrix_a = np.array([[1, 2], [3, 4]])
53matrix_b = np.array([[5, 6], [7, 8]])
54matrix_product = np.dot(matrix_a, matrix_b)
55print("Matrix Product:\n", matrix_product)
56
57# Matrix transpose
58transpose_matrix = np.transpose(matrix_a)
59print("Transpose of Matrix A:\n", transpose_matrix)
60

1pip install pandas

1import pandas as pd
2print(pd.__version__)
3

1import pandas as pd
2
3# Create a DataFrame from a dictionary
4data = {
5    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
6    'Age': [24, 27, 22, 32, 29],
7    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
8}
9
10df = pd.DataFrame(data)
11print("DataFrame:\n", df)
12
13# Save the DataFrame to a CSV file
14df.to_csv('data.csv', index=False)
15
16# Read data from the CSV file
17df_from_csv = pd.read_csv('data.csv')
18print("\nDataFrame from CSV:\n", df_from_csv)
19
20# Select specific columns
21selected_columns = df[['Name', 'City']]
22print("\nSelected Columns:\n", selected_columns)
23
24# Filter rows based on a condition
25filtered_rows = df[df['Age'] > 25]
26print("\nFiltered Rows (Age > 25):\n", filtered_rows)
27
28# Group data by 'City' and calculate the mean age
29grouped_data = df.groupby('City')['Age'].mean()
30print("\nGrouped Data (Mean Age by City):\n", grouped_data)
31
32# Add a new column with missing values
33df['Salary'] = [50000, 60000, None, 80000, None]
34print("\nDataFrame with Missing Values:\n", df)
35
36# Fill missing values with a specific value
37df_filled = df.fillna(0)
38print("\nDataFrame with Missing Values Filled:\n", df_filled)
39
40# Drop rows with missing values
41df_dropped = df.dropna()
42print("\nDataFrame with Missing Values Dropped:\n", df_dropped)
43

1import matplotlib.pyplot as plt
2import seaborn as sns
3

1import matplotlib.pyplot as plt
2import seaborn as sns

1import matplotlib.pyplot as plt
2import seaborn as sns
3import numpy as np
4import pandas as pd
5
6# Create a sample DataFrame
7np.random.seed(0)
8data = pd.DataFrame({
9    'Category': np.random.choice(['A', 'B', 'C'], size=100),
10    'Value1': np.random.randn(100),
11    'Value2': np.random.rand(100) * 100
12})
13
14# Line plot using Matplotlib
15plt.figure(figsize=(10, 5))
16plt.plot(data['Value1'], label='Value1')
17plt.plot(data['Value2'], label='Value2')
18plt.title('Line Plot using Matplotlib')
19plt.xlabel('Index')
20plt.ylabel('Values')
21plt.legend()
22plt.show()
23
24# Scatter plot using Matplotlib
25plt.figure(figsize=(10, 5))
26plt.scatter(data['Value1'], data['Value2'], c='blue', alpha=0.5)
27plt.title('Scatter Plot using Matplotlib')
28plt.xlabel('Value1')
29plt.ylabel('Value2')
30plt.show()
31
32# Bar plot using Seaborn
33plt.figure(figsize=(10, 5))
34sns.barplot(x='Category', y='Value2', data=data, ci=None)
35plt.title('Bar Plot using Seaborn')
36plt.xlabel('Category')
37plt.ylabel('Value2')
38plt.show()
39
40# Pair plot using Seaborn
41sns.pairplot(data, hue='Category')
42plt.suptitle('Pair Plot using Seaborn', y=1.02)
43plt.show()
44
45
46
47
48
49
50

1pip install scikit-learn
2

1import sklearn
2print(sklearn.__version__)
3

1pip install mysql-connector-python  /*install my sql connector*/
2
3import mysql.connector
4
5# Connect to the database
6conn = mysql.connector.connect(
7    host='localhost',
8    user='yourusername',
9    password='yourpassword',
10    database='yourdatabase'
11)
12
13# Create a cursor object
14cursor = conn.cursor()
15
16# Example query: create a table
17cursor.execute('''CREATE TABLE IF NOT EXISTS users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255), age INT)''')
18
19# Commit changes
20conn.commit()
21
22# Close the connection
23conn.close()
24