Practical CSV and Jupyter Data Extraction with Pandas
Case 2 — Data Extraction and Transformation
Basics of CSV & Jupyter
CSV – Comma Separated Values. Commas separate columns; missing values appear as blank entries or NaN. Jupyter Notebook rules – If running a cell in the middle, run all previous cells first. Keep the kernel up-to-date with earlier variable definitions and avoid running cells out of order to prevent NameError issues.
Volatility and Simple Calculations
Volatility: Defined as values above the median; the median splits the distribution at 50%. All new columns come from combining existing ones → “calculated insights”.
Selecting Data
- Select one column –
df["col"] - Select multiple columns –
df[["A", "B"]] - Select by position (iloc) –
df.iloc[row_index, col_index](example:df.iloc[0, 3]→ row 1, column 4) - Select by label (loc) –
df.loc[row_label, "ColumnName"]
Inspecting Data
Check top rows – df.head() shows the first 5 rows by default; df.head(3) shows the first 3. Summary statistics – df.describe() shows count, mean, std, min, 25%, 50% (median), 75%, max. Data types and missing counts – df.info() shows types and non-null counts; df.describe() provides statistics.
Handling Files and Interpreting Data
Columns vs rows – columns = variables, rows = observations. General CSV rules – Columns must be separated by commas; the first row often contains column names; blank values = missing. Use pd.read_csv("file").
Key Lessons from Case 2
Key lessons: Start simple then go complex; don’t jump into machine learning. Use simple statistics and plots first. Understand AI-generated code: read and understand the code first; code without understanding is dangerous in analysis. Data scientists must think, not just code. The value is in reasoning, interpreting, and communicating.
Case 3 — Data Transformation I
Business Understanding
Business understanding: Before doing analysis, always ask why a variable matters to the question. Data analysis must tie back to the business problem, not just coding.
Misleading Visualizations
Misleading visualizations: Visualizations can mislead if context is missing. Missing labels, improper scales, ignoring uncertainty, or showing only part of the data can lead to wrong conclusions.
Missing Values
Missing values: Missing values appear because the data was never collected, not reported, or not available for that country/year. Missing ≠ error. Missing does not mean Python failed or the file is corrupted.
Ethical Message
Ethical message: Always present the data honestly. Do NOT cherry-pick values. Show all sides. Transparency is essential in analysis and communication.
Creating New Columns
Creating new columns: The purpose is to generate new insights from existing variables. Examples: renewable percentage, ratios, differences. These help deepen analysis; they do not reduce dataset size or automatically fix missing data.
Groupby
Groupby: df.groupby("Column") groups data by categories. df.groupby("Country")["Value"].mean() gives the mean by country. df.groupby("A")["B"].agg(["min","mean","max"]) computes multiple statistics. Grouping is used to analyze each subgroup separately. Groupby + loop: for name, group in df.groupby("Country"): # name = group label, group = subset dataframe — used for separate plots, saving files, or separate analyses.
Pivot vs Groupby
Pivot vs Groupby: Groupby summarizes (mean, sum, min, max). Pivot reshapes data (long ↔ wide) and requires index, columns, values. Example: df.pivot(index="Year", columns="Country", values="Production").
String Operations
String operations (part of transformation): df["col"].str.split("-", expand=True) splits into new columns. df["col"].str.contains("text") detects patterns. df["col"].str.lower() cleans text.
Why Transform Data
Why transform data: To prepare for analysis, create derived variables, reshape the dataset, explore patterns, and prepare for visualization or modeling.
Key Lessons from Case 3
Key lessons: Business understanding comes first. Visualizations require context or they mislead. Missing values are normal and often come from real-world data limitations. Ethical analysis means communicating all aspects honestly. New columns = new insights (feature creation). Groupby allows subgroup analysis. Pivot allows reshaping for comparison and plotting. Transformations help reveal structure in messy data.
Case 4 — Data Transformation II
Merging Datasets
Merging datasets: Merging is important because it combines related information for deeper analysis. Use merges to bring multiple sources together. The goal is to enrich the data and allow more meaningful comparisons.
Histograms
Histograms: A histogram displays the distribution of a numerical variable. It shows how frequently values occur across ranges (bins). Used to examine skew, spread, and shape of the data, histograms use bars of varying heights to show the frequency of data points within specific, equal-width ranges called bins.
Why Dates Aren’t Numbers
Why Python can’t treat dates as numbers: Dates cannot be treated as simple numbers because numeric operations do not preserve real calendar meaning (e.g., months have different lengths). Dates need to be converted to datetime to work properly.
Visualization Clarity
Visualization clarity: Plots must be clear, self-contained, and easy to interpret. Include labels, titles, and readable axes. A good plot communicates the insight without needing extra explanation.
Scatter Plots
Scatter plots: A scatter plot is used to study relationships between two numerical variables. It helps reveal correlation, clusters, and patterns.
Common Plot Types
- Line plot → trends over time.
- Bar plot → comparing categories.
- Histogram → distribution of one numeric variable.
- Scatter plot → relationship between numeric variables.
- Box plot → distribution and quartiles.