Topic: Handling Datasets of All Types – Part 5 of 5: Working with Time Series and Tabular Data

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1. Understanding Time Series Data

• Time series data is a sequence of data points collected over time intervals.

• Examples: stock prices, weather data, sensor readings.

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2. Loading and Exploring Time Series Data

import pandas as pd

df = pd.read_csv('time_series.csv', parse_dates=['date'], index_col='date')
print(df.head())

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3. Key Time Series Concepts

• Trend: Long-term increase or decrease in data.

• Seasonality: Repeating patterns at regular intervals.

• Noise: Random variations.

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4. Preprocessing Time Series

• Handle missing data using forward/backward fill.

df.fillna(method='ffill', inplace=True)

• Resample data to different frequencies (daily, monthly).

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

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5. Working with Tabular Data

• Tabular data consists of rows (samples) and columns (features).

• Often requires handling missing values, encoding categorical variables, and scaling features (covered in previous parts).

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6. Summary

• Time series data requires special preprocessing due to temporal order.

• Tabular data is the most common format, needing cleaning and feature engineering.

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Exercise

• Load a time series dataset, fill missing values, and resample it monthly.

• For tabular data, encode categorical variables and scale numerical features.

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#TimeSeries #TabularData #DataScience #MachineLearning #Python

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