{"id":26903428,"url":"https://github.com/ahmedhosssam/lesser_pandas","last_synced_at":"2026-05-03T04:31:54.784Z","repository":{"id":285352309,"uuid":"957768444","full_name":"ahmedhosssam/lesser_pandas","owner":"ahmedhosssam","description":"Pandas-like Data Analysis library in 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Lesser Pandas\n![Logo](assets/lesser_pandas_logo.png)\n\n## Pandas-like Data Analysis Library in C++\n\n### Examples:\n\n```cpp\n#include \"lesser_pandas.h\"\n\nint main() {\n    DataFrame df(\"data.csv\");\n\n    cout \u003c\u003c \"Full DataFrame:\\n\";\n    cout \u003c\u003c df \u003c\u003c endl;\n\n    cout \u003c\u003c \"First 3 rows:\\n\";\n    df.head(3);\n\n    cout \u003c\u003c \"Last 2 rows:\\n\";\n    df.tail(2);\n\n    // Access a single column\n    Column\u0026 age_col = df[\"Age\"];\n    cout \u003c\u003c \"Age column:\\n\" \u003c\u003c age_col \u003c\u003c endl;\n\n    // Compute mean of Age column (ignoring missing values)\n    cout \u003c\u003c \"Mean age: \" \u003c\u003c age_col.mean() \u003c\u003c endl;\n\n    age_col.fillna(age_col.mean());\n    cout \u003c\u003c \"Age column after filling missing values with the average:\\n\" \u003c\u003c age_col \u003c\u003c endl;\n\n    // Save the DataFrame to a file named \"output.csv\"\n    df.save_to_csv(\"output.csv\", true, \"|\", true, \"N/A\", {\"Age\", \"Salary\"});\n\n    df.fillna(5);\n    cout \u003c\u003c \"DataFrame after filling missing values:\\n\";\n    cout \u003c\u003c df \u003c\u003c endl;\n\n    // Rename columns\n    df.rename({{\"Age\", \"Years\"}, {\"Salary\", \"Income\"}});\n    cout \u003c\u003c \"DataFrame after renaming columns:\\n\";\n    cout \u003c\u003c df \u003c\u003c endl;\n    cout \u003c\u003c df[\"Income\"];\n\n    cout \u003c\u003c \"Min Salary: \" \u003c\u003c df[\"Income\"].min() \u003c\u003c endl;\n    cout \u003c\u003c \"Max Salary: \" \u003c\u003c df[\"Income\"].max() \u003c\u003c endl;\n\n    // Filtering \n    DataFrame newData = df[df[\"Years\"] \u003e 30];\n    cout \u003c\u003c newData \u003c\u003c endl;\n\n    return 0;\n}\n```\n\n## TODO\n### Contributions are welcomed\n\n- [x] Rename a column\n- [x] `fillna`: Fill missing values\n- [x] `dropna(col_name)`: Drop rows where `col_name` is missing\n- [ ] `df.describe()`: Descriptive statistics\n- [ ] `df.corr()`: Correlation matrix\n- [x] `df[df['Amount'] \u003e 1000]`: Filter rows based on a condition\n- [x] `df.sum()`: Returns the sum of all rows\n- [ ] `df[\"col\"].sum()`\n  - If the column contains non-numeric data (e.g., strings), `sum()` will concatenate them.\n  - If the column has missing values (NaN), they will be ignored by default unless you specify `skipna=False`.\n- [x] `df.to_csv('cleaned_data.csv')` save a modified dataframe to a new csv file.\n- [ ] Implement A Test Suit for Lesser Pandas.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmedhosssam%2Flesser_pandas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahmedhosssam%2Flesser_pandas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmedhosssam%2Flesser_pandas/lists"}