Manual Data Analysis with Pandas #

Pandas is a powerful python library that allows for efficient data manipulation, analysis, and visualization. In this article, we will explore how to perform data analysis using pandas. We will also explore the possibility of automating the data analysis process using Google Apps Script.

Manual Data Analysis #

Pandas provides a convenient data structure called a DataFrame that allows us to store and manipulate data in a tabular format. To start with, we need to import the pandas library:

import pandas as pd

Loading Data #

The first step in data analysis is loading the data into a DataFrame. Pandas provides various functions to load data from different sources such as CSV files, Excel files, databases, etc. Here is an example of how to load a CSV file into a DataFrame:

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

Exploring Data #

Once the data is loaded, we can start exploring it. Some commonly used pandas functions for data exploration include:

  • head(): Returns the first n rows of the DataFrame.
  • info(): Provides information about the DataFrame, including the column names, data types, and non-null values.
  • describe(): Generates descriptive statistics of the data, such as count, mean, standard deviation, etc.
# Display the first 5 rows of the DataFrame
data.head()

# Get the summary information of the DataFrame
data.info()

# Generate descriptive statistics
data.describe()

Data Manipulation #

After exploring the data, we might need to clean and manipulate it to suit our analysis needs. Pandas provides a rich set of functions for data manipulation. Some common operations include:

  • Filtering: Selecting rows based on specific conditions.
  • Sorting: Sorting values based on one or more columns.
  • Grouping: Grouping data based on one or more columns.
  • Aggregation: Calculating summary statistics (e.g., sum, mean, count) for grouped data.
# Select rows with a specific condition
filtered_data = data[data['column_name'] > threshold]

# Sort values based on a column
sorted_data = data.sort_values(by='column_name')

# Group data based on a column and calculate mean
grouped_data = data.groupby('column_name').mean()

# Perform aggregation on grouped data
aggregated_data = grouped_data.agg({'column_name': 'sum', 'other_column': 'mean'})

Data Visualization #

Pandas integrates closely with other libraries like Matplotlib and Seaborn, which allow us to create visualizations to better understand the data. We can plot various types of charts, such as bar plots, line plots, scatter plots, etc.

import matplotlib.pyplot as plt

# Plot a bar chart
data.plot(kind='bar', x='column_name', y='another_column')

# Plot a line chart
data.plot(kind='line', x='column_name', y='another_column')

# Plot a scatter plot
data.plot(kind='scatter', x='column_name', y='another_column')

Automating Data Analysis with Google Apps Script #

If you are working with Google Sheets, you can automate the data analysis process using Google Apps Script. Although Google Apps Script is primarily based on JavaScript, it can interact with external libraries like pandas through the use of an API.

To get started, you need to enable the API and set up the necessary OAuth2 authentication. Once configured, you can write custom JavaScript code using the Script Editor in Google Sheets.

Here is an example of how to use Google Apps Script to perform a simple data analysis using pandas:

function analyzeData() {
// Load the data from the Google Sheet into a pandas DataFrame
var sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
var range = sheet.getDataRange().getValues();
var data = pandas.DataFrame(range, null, range[0]);

// Perform the desired data analysis operations
var filteredData = data[data['column_name'] > threshold];
var sortedData = data.sort_values('column_name');

// Write the results back to the Google Sheet
sheet.getRange(2, range[0].indexOf('filtered_column') + 1, filteredData.shape[0], filteredData.shape[1])
.setValues(filteredData.values);
sheet.getRange(2, range[0].indexOf('sorted_column') + 1, sortedData.shape[0], sortedData.shape[1])
.setValues(sortedData.values);
}

Use Case Examples #

E-commerce Analysis #

Pandas can be used to analyze sales data in e-commerce businesses. You can load the sales data into a DataFrame, perform various calculations like total sales, average order value, and create visualizations to identify patterns and trends.

Social Media Sentiment Analysis #

Pandas can also be used to analyze sentiment data from social media platforms. By loading the data into a DataFrame, you can perform sentiment analysis, grouping data by sentiment score, and creating visualizations to understand the overall sentiment of user comments.

Stock Market Analysis #

Pandas is widely used for stock market analysis. You can load historical stock data into a DataFrame, calculate the daily returns, perform statistical analysis, and create visualizations like candlestick charts, moving averages, and trend lines.

In conclusion, pandas is a versatile library for data analysis that provides powerful tools for data manipulation, exploration, and visualization. Whether you prefer manual analysis or automation with Google Apps Script, pandas can greatly enhance your data analysis workflows.