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Time Series Forecasting with Pandas¶

Learn how Python handles time-based data and lays the foundation for forecasting¶


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Why it’s so important¶

Time series data is everywhere; stock prices, weather reports, sensor data, website traffic. If you can master how to work with time, you can unlock powerful insights and predict the future.

Pandas makes it incredibly intuitive to handle time-indexed data, re-sample it, and prep it for machine learning models.

In this article, let’s explore Pandas’s core time series operations, laying the groundwork for real-world forecasting models.

Key Concepts Covered¶

How DateTimeIndex supercharges time-aware operations
Resampling vs. frequency conversion: resample() vs. asfreq()
Rolling windows and moving averages for smoothing
Time-shifting data for lag analysis
Handling missing time entries cleanly

Fun Fact¶

Pandas can automatically parse date columns and even infer frequencies, making it possible to clean and analyze time data with just a few lines of code.

Implementation¶

In [ ]:

import pandas as pd
import numpy as np

# Create time series data
dates = pd.date_range(start="2023-01-01", periods=10, freq="D")
data = np.random.randint(100, 200, size=(10,))
df = pd.DataFrame({'value': data}, index=dates)

# Resample to every 3 days and get the mean
resampled = df.resample('3D').mean()

# Calculate rolling average
df['rolling_avg'] = df['value'].rolling(window=3).mean()

# Shift data for comparison (e.g., yesterday’s value)
df['prev_day'] = df['value'].shift(1)

print(df)
print("Resampled:\n", resampled)

            value  rolling_avg  prev_day
2023-01-01    173          NaN       NaN
2023-01-02    139          NaN     173.0
2023-01-03    176   162.666667     139.0
2023-01-04    111   142.000000     176.0
2023-01-05    172   153.000000     111.0
2023-01-06    164   149.000000     172.0
2023-01-07    179   171.666667     164.0
2023-01-08    149   164.000000     179.0
2023-01-09    109   145.666667     149.0
2023-01-10    107   121.666667     109.0
Resampled:
                  value
2023-01-01  162.666667
2023-01-04  149.000000
2023-01-07  145.666667
2023-01-10  107.000000

*This is the backbone of forecasting models. Whether you’re predicting sales or analyzing sensor patterns, these time-based manipulations are the first step toward insights!*

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