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Analyze Financial Data with Python - Introduction to Pandas

Analyze Financial Data with Python - Introduction to Pandas

Getting Started with Pandas

Using the apply() Function in Pandas

The `apply()` function in Pandas lets you apply a function to every item in a column or row of a DataFrame, which can be useful for transforming data.

                                

import pandas as pd
# Function to double a number

def double(x):
  return 2 * x

# Create a DataFrame

 df = pd.DataFrame({'column1': [1, 2, 3], 'column2': [4, 5, 6]})

# Apply the doubling function to 'column1'

 df['column1'] = df['column1'].apply(double)

# Apply a lambda function to 'column2'

 df['column2'] = df['column2'].apply(lambda x: 3 * x)

# Apply a function to each row

def custom_func(row):
  return row['column1'] * 1.5 + row['column2']

df['newColumn'] = df.apply(custom_func, axis=1)

Adding Columns to a DataFrame

You can add new columns to a DataFrame by assigning values directly. This can be done by either setting a single value for all rows or by performing calculations on existing columns.

                                

import pandas as pd
# Create a DataFrame
 df = pd.DataFrame({'oldColumn': [10, 20, 30]})

# Add a new column with specific values
 df['newColumn'] = [1, 2, 3]

# Add a new column with the same value for all rows
 df['newColumn'] = 1

# Create a new column by calculating from an existing column
 df['newColumn'] = df['oldColumn'] * 5

Creating DataFrames in Pandas

You can create a Pandas DataFrame from various data sources, including dictionaries, lists, and CSV files. Each method allows you to organize data into a table format.

                                

import pandas as pd
# Create a DataFrame from a dictionary
 data = {'name': ['Anthony', 'Maria'], 'age': [30, 28]}
df = pd.DataFrame(data)

# Create a DataFrame from a list of lists
 data = [['Tom', 20], ['Jack', 30], ['Meera', 25]]
df = pd.DataFrame(data, columns=['Name', 'Age'])

# Create a DataFrame by reading from a CSV file
 df = pd.read_csv('students.csv')

Understanding Pandas DataFrames

A DataFrame is the primary data structure in Pandas. It is a 2D table where you can store and manipulate data in rows and columns.

                                

import pandas as pd
# Importing the Pandas library

Using Aggregates in Pandas

Grouping Data with Pandas

The `groupby()` function in Pandas groups data by one or more columns and allows you to perform aggregate operations, such as calculating the average of a column.

                                

import pandas as pd
# Create a DataFrame
 df = pd.DataFrame([
  ['Amy', 'Assignment 1', 75],
  ['Amy', 'Assignment 2', 35],
  ['Bob', 'Assignment 1', 99],
  ['Bob', 'Assignment 2', 35]
], columns=['Name', 'Assignment', 'Grade'])

# Group by 'Name' and calculate the mean grade
 result = df.groupby('Name').Grade.mean()
print(result)
# Output:
# Name
# Amy    55.0
# Bob    67.0
# Name: Grade, dtype: float64

Calculating Statistics in Pandas

Pandas provides functions to calculate statistics for each column, such as mean, median, and maximum. These functions help you quickly analyze data in your DataFrame.

                                

import pandas as pd
# Create a DataFrame
 df = pd.DataFrame({'columnName': [10, 20, 30, 40]})

# Calculate various statistics
 mean = df.columnName.mean() # Average value
 std = df.columnName.std()   # Standard deviation
 median = df.columnName.median() # Median value
 max_value = df.columnName.max() # Maximum value
 min_value = df.columnName.min() # Minimum value
 count = df.columnName.count() # Number of values
 unique_count = df.columnName.nunique() # Number of unique values
 unique_values = df.columnName.unique() # List of unique values

print(mean, std, median, max_value, min_value, count, unique_count, unique_values)
# Output: 25.0 12.91 25.0 40 10 4 4 [10 20 30 40]

Loading Financial Data

Working with Dates and Times in Python

Python's `datetime` module helps you manage dates and times. You can create specific dates and times, and work with them in your code.

                                

import datetime
# Create a date
 date = datetime.date(year=2019, month=2, day=16)
print(date) # Output: 2019-02-16

# Create a time
 time = datetime.time(hour=13, minute=48, second=5)
print(time) # Output: 13:48:05

# Create a datetime
 timestamp = datetime.datetime(year=2019, month=2, day=16, hour=13, minute=48, second=5)
print(timestamp) # Output: 2019-02-16 13:48:05

Creating DataFrames from CSV Files

Pandas can load data from CSV files into a DataFrame, making it easy to work with large datasets from files.

                                

import pandas as pd
# Load data from a CSV file into a DataFrame
 df = pd.read_csv('data.csv')
print(df)
# Output: DataFrame created from the CSV file data

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