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diff --git a/tutorials/module_4/4.3 Importing and Managing Data.md b/tutorials/module_4/4.3 Importing and Managing Data.md index 101d5ab..ef44a7a 100644 --- a/tutorials/module_4/4.3 Importing and Managing Data.md +++ b/tutorials/module_4/4.3 Importing and Managing Data.md @@ -85,48 +85,6 @@ df.to_csv("edited_experiment.csv", index=False) This workflow makes pandas ideal for working with tabular data, you can quickly edit or generate datasets, verify values, and save clean, structured files for later visualization or analysis. -## Subsetting and Conditional filtering -You can select rows, columns, or specific conditions from a DataFrame. - -```python -# Select a column -force = df["Force_N"] - -# Select multiple columns -subset = df[["Time_s", "Force_N"]] - -# Conditional filtering -df_high_force = df[df["Force_N"] > 50] -``` - - -![[Pasted image 20251013064718.png]] - -## Combining and Merging Datasets -Often, multiple sensors or experiments must be merged into one dataset for analysis. - -```python -# Merge on a common column (e.g., time) -merged = pd.merge(df_force, df_temp, on="Time_s") - -# Stack multiple test runs vertically -combined = pd.concat([df_run1, df_run2], axis=0) -``` - - -## Problem 1: Describe a dataset -Use pandas built-in describe data to report on the statistical mean of the given experimental data. - -```python -import matplotlib.pyplot as plt - -plt.plot(df["Time_s"], df["Force_N"]) -plt.xlabel("Time (s)") -plt.ylabel("Force (N)") -plt.title("Force vs. Time") -plt.show() -``` - ### Problem 2: Import time stamped data |