README.md

Project 73 - Analyzing Programming Languages

This project explores trends in programming languages over time using data from StackOverflow. We focus on visualizing the popularity of various languages and applying techniques to smooth time series data for clearer trend analysis.

(README generated by AI)

Dataset

The dataset used in this project (QueryResults (1).csv) contains monthly counts of posts for different programming languages on StackOverflow. It has the following columns:

m: Date of the month (converted to datetime for analysis)
TagName: Programming language name
Unnamed: 2: Number of posts in that month

Setup

The project uses Python with the following libraries:

```python import pandas as pd import matplotlib.pyplot as plt import datetime as dt ````

Steps

1. Data Loading

The CSV file is read into a Pandas DataFrame:

python data_frame = pd.read_csv("QueryResults (1).csv")

2. Data Exploration

Check the first and last rows: data_frame.head(), data_frame.tail()
Check DataFrame dimensions: data_frame.shape
Count non-null entries per column: data_frame.count()
Find total posts per language: use data_frame.groupby("TagName")["Unnamed: 2"].sum()
Find languages with fewest months of data: use data_frame.groupby("TagName").count().idxmin()

3. Data Cleaning

Convert the m column to datetime for better plotting:

python data_frame['m'] = pd.to_datetime(data_frame['m'])

4. Data Visualization

Using Matplotlib to plot the number of posts per language over time:

```python languages = [] for lang in data_frame['TagName']: if lang not in languages: languages.append(lang)

plt.figure(figsize=(12, 6)) for lang in languages: lang_data = data_frame[data_frame['TagName'] == lang] x = lang_data['m'] y = lang_data['Unnamed: 2'].rolling(window=9).mean() # rolling mean smoothing plt.plot(x, y, label=lang)

plt.xlabel('Date') plt.ylabel('Number of Posts') plt.title('Posts Over Time') plt.legend() plt.show() ```

5. Smoothing Time Series

To reduce noise in monthly post counts, a rolling mean is applied:

python y_smoothed = lang_data['Unnamed: 2'].rolling(window=6, min_periods=1).mean()

This helps reveal long-term trends without short-term fluctuations.

Notes

The rolling mean window can be adjusted (6, 9, 12 months) depending on how smooth you want the trend.
Matplotlib styling (colors, markers, line styles) can be customized for better readability.