Data Cleaning with Pandas in Python

• Goal: Load a specific dataset from a URL and identify data quality issues (missing values, wrong dtypes, duplicates).

  • Dataset URL: https://raw.githubusercontent.com/pplonski/datasets-for-start/refs/heads/master/world_happiness_report/WHR_2024.csv

  • Constraints:

    • One execution step at a time.
    • One Python code block per response.
    • No $ for currency.
    • Load and inspect only (shape, columns, head) first.
    • Iterative approach.

  • The user wants to load the data and check for quality issues.

  • According to the rules: "When a dataset is not yet loaded in current notebook context, first load and inspect only: show shape, columns, and head()."

  • "Do not combine first-load data inspection with plotting/modeling in the same response."

  • Wait, the user asked for missing values, dtypes, and duplicates too. However, the specific "Notebook rules" state: "first load and inspect only: show shape, columns, and head()."

  • Therefore, the very first step should be loading the CSV and performing basic inspection.

  • Import pandas.

  • Read the CSV from the URL.

  • Display df.shape.

  • Display df.columns.

  • Display df.head().

  • Explain what I'm doing (loading the data).

  • Provide the code.

The user wants to load the dataset and identify data quality issues (missing values, wrong dtypes, duplicates).

Current state analysis:

• Dataset loaded: Yes.
• Missing values checked: Yes. region has 6 missing, and almost all other numeric columns have 3 missing.
• Dtypes checked: Yes. country and region are strings, the rest are float64. This seems correct for the nature of the data.
• Duplicates checked: Yes. 0 duplicates found.

The original goal was: "load the dataset ... and identify data quality issues: missing values, wrong dtypes, duplicates".

The current outputs provide:

• Missing values count for each column.
• Data types for each column.
• Count of duplicate rows.

All parts of the user's request have been addressed. I can now provide the final answer.

Insights:

• Missing values: There are 6 missing values in the region column and 3 missing values in each of the numerical columns (gdp_per_capita, social_support, healthy_life_expectancy, freedom_to_make_life_choices, generosity, and perceptions_of_corruption).
• Data types: The types are correct. Categorical columns (country, region) are strings, and the metrics are floats.
• Duplicates: There are no duplicate rows in the dataset.

Decision: Path A.

The user wants to clean the dataset based on two specific rules:

1. Fill missing numeric values with their respective column medians.
2. Drop columns where more than 50% of the values are missing.

Looking at the previous output:

• region has 6 missing values out of 143 (~4.2%).
• All numeric columns (gdp_per_capita, social_support, healthy_life_expectancy, freedom_to_make_life_choices, generosity, perceptions_of_corruption) have 3 missing values out of 143 (~2.1%).
• No column has >50% missing values, so no columns should be dropped.

Plan:

1. Identify numeric columns.
2. Calculate medians for those columns.
3. Fill NaNs in those columns with the calculated medians.
4. (Implicitly) the drop logic won't trigger but I should include it to follow the user's request.

Wait, the prompt says "fill missing numeric values with column medians". The numeric columns are: happiness_score (0 missing), gdp_per_capita, social_support, healthy_life_expectancy, freedom_to_make_life_choices, generosity, perceptions_of_corruption.

Let's implement this.

• Original Goal: "fill missing numeric values with column medians and drop columns with >50% missing".

  • Notebook state analysis:

    • Cell 1: Loaded dataset.
    • Cell 2: Identified missing values (region: 6, others: 3).
    • Cell 3:
      • Identified numeric columns.
      • Filled numeric missing values with medians.
      • Dropped columns with > 50% missing values (none met this threshold).
      • Verified results using df.isnull().sum().
    • Output 3: Shows region still has 6 missing values, but all other columns (including all numeric ones) have 0 missing values.

  • Numeric values were successfully filled with medians.

  • No columns were dropped because none had > 50% missing values (the maximum was 6/143 for region).

  • The final state matches the requested goal.

  • Path A: The outputs are sufficient to answer the goal.

  • State: Numeric columns (gdp_per_capita, social_support, healthy_life_expectancy, freedom_to_make_life_choices, generosity, perceptions_of_corruption) now have 0 missing values.

  • State: The region column still has 6 missing values, as it is a categorical string column and the goal specifically requested filling "numeric values".

  • State: No columns were dropped as none exceeded the 50% missing threshold.

The user wants to:

1. Rename columns to snake_case.
2. Convert any string numbers (numeric values stored as strings) to floats.

Looking at the current notebook state:

• Columns are: ['country', 'region', 'happiness_score', 'gdp_per_capita', 'social_support', 'healthy_life_expectancy', 'freedom_to_make_life_choices', 'generosity', 'perceptions_of_corruption'].
• Most columns are already in snake_case.
• Data types: country (str), region (str), others (float64).

Check:

• Are any columns not in snake_case? They all look like snake_case already. However, to be robust, I should apply a transformation or check if any need changing. In this specific dataset, they are already snake_case.
• Are there "string numbers"? The dtype output in output-2 shows the numeric columns are already float64. The string columns are country and region. It's unlikely those contain "string numbers" in a standard way, but I should verify or attempt a conversion.

Plan:

1. Rename columns to ensure they are snake_case (though they seem to be, I'll apply a standard lowercase/replace space approach to be safe).
2. Attempt to convert any columns that are objects but contain numeric-looking data to floats.

Wait, the columns are already happiness_score, gdp_per_capita, etc. They are already snake_case. I will write a small utility to ensure they are snake_case and then use pd.to_numeric with errors='ignore' on object columns to catch any "string numbers".

Step 1: Handle renaming (even if it looks done, I'll ensure it). Step 2: Convert string numbers to float.

Since the columns are already snake_case, I'll double check if there are any hidden spaces or mixed casing. Actually, looking at the output, they are already perfect. But the user asked for it, so I should implement the logic.

Correct approach for snake_case: Lowercase and replace spaces with underscores. Correct approach for string numbers: pd.to_numeric(..., errors='ignore').

Let's execute.

• Goal: Rename columns to snake_case and convert any string numbers to float.

  • Notebook State Analysis:

    • [cell-4] was executed.
    • df.columns were converted to lowercase and spaces replaced with underscores. The output shows columns like country, region, happiness_score, etc., which are already in snake_case.
    • The code iterated through object columns, used pd.to_numeric(df[col], errors='coerce'), and updated the column if numeric values were found.
    • [output-4] displays df.dtypes. The columns that were expected to be floats (like happiness_score, gdp_per_capita, etc.) are already float64. The country and region columns are str, which is correct as they are text.

  • Sufficiency: The goal "rename columns to snake_case" is achieved (verified by df.columns output). The goal "convert any string numbers to float" is achieved (verified by df.dtypes output; all numeric columns are float64 and text columns remain str).

  • The output shows the column names are already lowercase and underscores are used.

  • The data types for the numeric columns are float64, and the non-numeric columns (country, region) are str.

  • The process is complete.

  • Since the outputs are sufficient, I choose Path A.

  • Provide a natural final answer.

  • Include <!--final answer-->.

The user wants a "before/after summary" of the cleaned dataset based on the provided notebook state.

*   Cell 1: Data loaded from CSV. Original columns and shape established.
*   Cell 2: Missing values identified (some in `region`, `gdp_per_capita`, etc.).
*   Cell 3: Numeric missing values filled with medians.
*   Cell 4: Columns renamed to snake_case and string-numeric conversion attempted.

*   Column names: Mixed case/spaces $\rightarrow$ snake_case.
*   Missing values: Present $\rightarrow$ Filled (numeric) / Still present (categorical - `region`).
*   Data types: Some might have been objects $\rightarrow$ floats (though looking at the output of cell 2, most were already float64, except country/region).

*   A "before/after" summary usually implies comparing the state of the data (nulls, shapes, column names) from the start of the notebook to the end.
*   Since the original dataframe state is lost (the variable `df` was modified in place), I need to recreate the "before" summary using the knowledge from the cell outputs or by reloading the data if necessary. However, it's better to create a summary table that lists the key changes.
*   Actually, the most effective way to show a summary is a table comparing:
    1.  Initial null count vs. Final null count.
    2.  Initial column names vs. Final column names.
    3.  Initial dtypes vs. Final dtypes.

*   *Refinement:* I cannot realistically "reload" the data in a single step and keep the current state without risking confusion. I should construct a summary based on the variables currently in memory and the known initial state from the previous outputs.

*   Wait, looking at the prompt "show a before/after summary", I can't easily get the *exact* "before" dataframe unless I reload it. To be accurate, I should reload the original data into a temporary variable `df_orig` to compare it with the current `df`.

*   *Plan:*
    1. Reload original data.
    2. Compare shapes, null counts, and column names.
    3. Display this as a summary table.

*   *Constraint Check:* "Return at most one Python code block per response."

*   *Drafting the code:*
    *   `df_orig = pd.read_csv(url)`
    *   Compare `df_orig.isnull().sum()` vs `df.isnull().sum()`.
    *   Compare `df_orig.columns` vs `df.columns`.

*   *Actually*, a simpler "Before vs After" can be a dataframe with columns: `Feature`, `Before (Nulls)`, `After (Nulls)`, `Before (Type)`, `After (Type)`.

*   Wait, the user might just want a visual summary of what changed. Let's go with a comparison table.

*   Columns: `Column`, `Original Name`, `Cleaned Name`, `Original Nulls`, `Cleaned Nulls`, `Original Dtype`, `Cleaned Dtype`.

*   *Steps:*
    1. Reload original data to get the baseline.
    2. Build the summary dataframe.
    3. Display it.

The user wants a before/after summary of the cleaned dataset. The notebook state shows cell-5 was executed, which created a summary_df comparing the original dataset (df_orig) with the cleaned dataset (df). The output-5 shows the first few columns of this summary table:

• Cleaned Column vs Original Column: They are mostly identical (lowercase/snake_case was applied, but in this specific dataset, they were already quite close or identical, though the goal was to ensure snake_case).
• Original Nulls vs Cleaned Nulls: For gdp_per_capita, social_support, etc., original nulls were 3, and cleaned nulls are 0. This confirms the median imputation worked.
• Original Dtype vs Cleaned Dtype: The output is truncated, but the logic in cell-4 and the general context of cell-5 suggests the comparison is being made.

The goal was "show a before/after summary". cell-5 did exactly that. The output is truncated in the provided text, but in a real notebook, display(summary_df) would show the full table. I have enough information to summarize the changes:

1. Missing values: Numeric columns like gdp_per_capita and social_support had 3 missing values each, which are now 0.
2. Column names: Standardized to snake_case.
3. Data types: Numeric columns are now consistently float64.

Wait, looking at output-5, it's truncated. But as an AI analyst, I can see the logic and the partial results. The user's request was to "show" it. It has been shown in the notebook. I can now synthesize the findings.

Actually, the prompt asks me to analyze the latest executed notebook outputs. The summary_df in output-5 clearly shows:

• Original Nulls for several columns were 3, while Cleaned Nulls are 0.
• The mapping between Original Column and Cleaned Column is provided.
• Dtypes are compared.

The goal is fully met by the output of cell-5. I will provide a natural final answer.