What Are Python Pandas?
Use Cases for API Responses
(Part 2)

Introduction

In today's data-driven world, organizations increasingly rely on APIs to retrieve and analyze data from various sources. In Part 1 of this series, we introduced you to Python's powerful Pandas library and demonstrated how it transforms raw API responses into structured data you can analyze and act upon - if you haven't checked it out yet, you can read it here: "What are Python Pandas? How do Pandas turn API responses into insights".

In this second installment, we dig deeper into real-world use cases, showing how Pandas + APIs can streamline workflows like financial analysis, social media monitoring, eCommerce reporting, and more. You'll see step-by-step how to fetch API data and leverage Pandas to generate meaningful insights tailored to your projects.

Setting Up Environment

Installing Necessary Libraries

To work with API data, we primarily need the following Python libraries:

• Pandas: For data manipulation and analysis.
• Requests: To make API calls.
• Matplotlib & Seaborn: For visualization.

pip install pandas requests matplotlib seaborn

Introduction to Jupyter Notebook

pip install jupyter
jupyter notebook

Obtaining API Data

Finding Suitable APIs for Analysis

Commonly used APIs include OpenWeather, CoinGecko, Alpha Vantage, and COVID-19 API.

Using the Requests Library

import requests
API_KEY = 'your_api_key'
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=TSLA&apikey={API_KEY}'
response = requests.get(url)
data = response.json()
print(data)

Data Preprocessing

Converting API Data into a Pandas DataFrame

import pandas as pd
df = pd.DataFrame.from_dict(data['Time Series (Daily)'], orient='index')
df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
df = df.astype(float)
df.index = pd.to_datetime(df.index)

Data Exploration and Analysis

Generating Descriptive Statistics

print(df.describe())

Visualizing Data

import matplotlib.pyplot as plt
plt.plot(df.index, df['Close'])
plt.title('Stock Closing Prices Over Time')
plt.show()

Advanced Data Analysis Techniques

Grouping and Aggregating Data

monthly_avg = df.resample('M').mean()

Applying Filters

high_volatility = df[df['High'] - df['Low'] > 10]

Case Study: Analyzing Stock Market Data

Obtaining Stock Market Data

url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=TSLA&apikey={API_KEY}'
response = requests.get(url)
data = response.json()
df = pd.DataFrame.from_dict(data['Time Series (Daily)'], orient='index')

Conclusion

API data analysis involves retrieving, cleaning, and analyzing data efficiently. Potential applications include finance, healthcare, e-commerce, and social media analytics.

FAQ

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