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one","series-vs-dataframe",5,"content:pandas:5.series-vs-dataframe.md","pandas/5.series-vs-dataframe.md","pandas/5.series-vs-dataframe",{"_path":59,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":60,"description":61,"slug":62,"parentId":5,"order":63,"_type":14,"_id":64,"_source":16,"_file":65,"_stem":66,"_extension":19},"/pandas/basic-dataframe-info","Basic DataFrame Info","Learn how to explore and get basic information about your DataFrames to understand your data","basic-dataframe-info",6,"content:pandas:6.basic-dataframe-info.md","pandas/6.basic-dataframe-info.md","pandas/6.basic-dataframe-info",{"_path":68,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":69,"description":70,"slug":71,"order":72,"isParent":12,"icon":73,"_type":14,"_id":74,"_source":16,"_file":75,"_stem":76,"_extension":19,"children":77,"expanded":12},"/pandas/loading-data","Loading Data","Learn how to load data from various sources into Pandas DataFrames","loading-data",7,"pi 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summaries","data-shape-and-statistics",13,"content:pandas:13.data-shape-and-statistics.md","pandas/13.data-shape-and-statistics.md","pandas/13.data-shape-and-statistics",{"_path":135,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":136,"description":137,"slug":138,"parentId":106,"order":139,"_type":14,"_id":140,"_source":16,"_file":141,"_stem":142,"_extension":19},"/pandas/column-information-and-data-types","Column Information and Data Types","Learn how to explore individual columns and understand different data types in your DataFrames","column-information-and-data-types",14,"content:pandas:14.column-information-and-data-types.md","pandas/14.column-information-and-data-types.md","pandas/14.column-information-and-data-types",{"_path":144,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":145,"description":146,"slug":147,"order":148,"isParent":12,"icon":149,"_type":14,"_id":150,"_source":16,"_file":151,"_stem":152,"_extension":19,"children":153},"/pandas/selecting-data","Selecting Data","Learn how to select specific columns, rows, and subsets of data from your DataFrames","selecting-data",15,"pi pi-filter","content:pandas:15.selecting-data.md","pandas/15.selecting-data.md","pandas/15.selecting-data",[154,163,172],{"_path":155,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":156,"description":157,"slug":158,"parentId":144,"order":159,"_type":14,"_id":160,"_source":16,"_file":161,"_stem":162,"_extension":19},"/pandas/selecting-columns-and-rows","Selecting Columns and Rows","Learn how to select specific 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names and logical data ordering","renaming-and-sorting-data",27,"content:pandas:27.renaming-and-sorting-data.md","pandas/27.renaming-and-sorting-data.md","pandas/27.renaming-and-sorting-data",{"_path":267,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":268,"description":269,"slug":270,"order":271,"isParent":12,"icon":234,"_type":14,"_id":272,"_source":16,"_file":273,"_stem":274,"_extension":19,"children":275},"/pandas/grouping-and-aggregation","Grouping and Aggregation","Learn to group data and calculate summary statistics for powerful data analysis","grouping-and-aggregation",28,"content:pandas:28.grouping-and-aggregation.md","pandas/28.grouping-and-aggregation.md","pandas/28.grouping-and-aggregation",[276,285,294],{"_path":277,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":278,"description":279,"slug":280,"parentId":267,"order":281,"_type":14,"_id":282,"_source":16,"_file":283,"_stem":284,"_extension":19},"/pandas/groupby-operations","GroupBy Operations","Learn the 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tables and cross-tabulations for analysis","pivot-tables-and-cross-tabulation",31,"content:pandas:31.pivot-tables-and-cross-tabulation.md","pandas/31.pivot-tables-and-cross-tabulation.md","pandas/31.pivot-tables-and-cross-tabulation",{"_path":304,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":305,"description":306,"slug":307,"order":308,"isParent":12,"icon":234,"_type":14,"_id":309,"_source":16,"_file":310,"_stem":311,"_extension":19,"children":312},"/pandas/combining-data","Combining Data","Learn to combine data from multiple DataFrames for comprehensive analysis","combining-data",32,"content:pandas:32.combining-data.md","pandas/32.combining-data.md","pandas/32.combining-data",[313,322],{"_path":314,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":315,"description":316,"slug":317,"parentId":304,"order":318,"_type":14,"_id":319,"_source":16,"_file":320,"_stem":321,"_extension":19},"/pandas/concatenating-dataframes","Concatenating DataFrames","Learn to stack DataFrames vertically and horizontally for simple data combination","concatenating-dataframes",33,"content:pandas:33.concatenating-dataframes.md","pandas/33.concatenating-dataframes.md","pandas/33.concatenating-dataframes",{"_path":323,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":324,"description":325,"slug":326,"parentId":304,"order":327,"_type":14,"_id":328,"_source":16,"_file":329,"_stem":330,"_extension":19},"/pandas/merging-and-join-operations","Merging and Join Operations","Master different types of joins to combine related data from multiple sources","merging-and-join-operations",34,"content:pandas:34.merging-and-join-operations.md","pandas/34.merging-and-join-operations.md","pandas/34.merging-and-join-operations",{"_path":332,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":333,"description":334,"slug":335,"order":336,"isParent":12,"icon":234,"_type":14,"_id":337,"_source":16,"_file":338,"_stem":339,"_extension":19,"children":340},"/pandas/working-with-dates","Working with Dates","Master date and time operations for time-based analysis and reporting","working-with-dates",35,"content:pandas:35.working-with-dates.md","pandas/35.working-with-dates.md","pandas/35.working-with-dates",[341,350],{"_path":342,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":343,"description":344,"slug":345,"parentId":332,"order":346,"_type":14,"_id":347,"_source":16,"_file":348,"_stem":349,"_extension":19},"/pandas/datetime-operations","DateTime Operations","Learn to convert, manipulate, and extract information from dates and times","datetime-operations",36,"content:pandas:36.datetime-operations.md","pandas/36.datetime-operations.md","pandas/36.datetime-operations",{"_path":351,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":352,"description":353,"slug":354,"parentId":332,"order":355,"_type":14,"_id":356,"_source":16,"_file":357,"_stem":358,"_extension":19},"/pandas/time-series-and-resampling","Time Series and Resampling","Discover how to analyze data over time periods and aggregate by different time intervals","time-series-and-resampling",37,"content:pandas:37.time-series-and-resampling.md","pandas/37.time-series-and-resampling.md","pandas/37.time-series-and-resampling",{"_path":360,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":361,"description":362,"slug":363,"order":364,"isParent":12,"icon":234,"_type":14,"_id":365,"_source":16,"_file":366,"_stem":367,"_extension":19,"children":368},"/pandas/saving-your-work","Saving Your Work","Learn to export your analyzed data to different formats for sharing and reporting","saving-your-work",38,"content:pandas:38.saving-your-work.md","pandas/38.saving-your-work.md","pandas/38.saving-your-work",[369,378],{"_path":370,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":371,"description":372,"slug":373,"parentId":360,"order":374,"_type":14,"_id":375,"_source":16,"_file":376,"_stem":377,"_extension":19},"/pandas/exporting-data","Exporting Data (CSV, Excel, JSON)","Learn to save your DataFrames to common file formats for sharing and reporting","exporting-data",39,"content:pandas:39.exporting-data.md","pandas/39.exporting-data.md","pandas/39.exporting-data",{"_path":379,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":380,"description":381,"slug":382,"parentId":360,"order":383,"_type":14,"_id":384,"_source":16,"_file":385,"_stem":386,"_extension":19},"/pandas/database-export","Database Export","Discover how to save data back to databases for integration with other systems","database-export",40,"content:pandas:40.database-export.md","pandas/40.database-export.md","pandas/40.database-export",[96],{"_path":97,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":98,"description":99,"slug":100,"parentId":68,"order":101,"body":389,"_type":14,"_id":102,"_source":16,"_file":103,"_stem":104,"_extension":19},{"type":390,"children":391,"toc":5685},"root",[392,401,407,463,470,475,1085,1091,1096,1851,1857,1864,2191,2197,2787,2793,3386,3392,3397,5161,5167,5355,5361,5649,5655,5660,5674,5679],{"type":393,"tag":394,"props":395,"children":397},"element","h1",{"id":396},"️-handling-file-errors",[398],{"type":399,"value":400},"text","⚠️ Handling File Errors",{"type":393,"tag":402,"props":403,"children":404},"p",{},[405],{"type":399,"value":406},"File loading often goes wrong! Files might be missing, corrupted, or formatted differently than expected. Let's learn how to handle these errors gracefully and troubleshoot common problems.",{"type":393,"tag":408,"props":409,"children":413},"info-box",{"emoji":410,"title":411,"type":412},"🚨","Why Files Cause Problems","warning",[414,423,453],{"type":393,"tag":402,"props":415,"children":416},{},[417],{"type":393,"tag":418,"props":419,"children":420},"strong",{},[421],{"type":399,"value":422},"Common File Issues:",{"type":393,"tag":424,"props":425,"children":426},"ul",{},[427,433,438,443,448],{"type":393,"tag":428,"props":429,"children":430},"li",{},[431],{"type":399,"value":432},"File doesn't exist or wrong path",{"type":393,"tag":428,"props":434,"children":435},{},[436],{"type":399,"value":437},"File is open in another program (Excel)",{"type":393,"tag":428,"props":439,"children":440},{},[441],{"type":399,"value":442},"Wrong file format or encoding",{"type":393,"tag":428,"props":444,"children":445},{},[446],{"type":399,"value":447},"Malformed data or missing 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handling\"\"\"\n    try:\n        df = pd.read_csv(filename)\n        print(f\"✅ Successfully loaded {filename}\")\n        print(f\"   Shape: {df.shape}\")\n        return df\n    \n    except FileNotFoundError:\n        print(f\"❌ File '{filename}' not found!\")\n        return None\n    \n    except Exception as e:\n        print(f\"❌ Error loading {filename}: {e}\")\n        return None\n\n# Test with non-existent file\ndf = load_file_safely('nonexistent.csv')\n\n# Test with real file (create one first)\nsample_data = \"name,age\\nAlice,25\\nBob,30\"\nwith open('test.csv', 'w') as f:\n    f.write(sample_data)\n\ndf = load_file_safely('test.csv')\nif df is not None:\n    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engine='openpyxl')\"",{"type":393,"tag":491,"props":3363,"children":3364},{"style":503},[3365],{"type":399,"value":632},{"type":393,"tag":491,"props":3367,"children":3368},{"class":493,"line":346},[3369,3373,3377,3382],{"type":393,"tag":491,"props":3370,"children":3371},{"style":589},[3372],{"type":399,"value":2005},{"type":393,"tag":491,"props":3374,"children":3375},{"style":503},[3376],{"type":399,"value":597},{"type":393,"tag":491,"props":3378,"children":3379},{"style":549},[3380],{"type":399,"value":3381},"\"3. df = pd.read_excel('file.xlsx', sheet_name='Sheet1')\"",{"type":393,"tag":491,"props":3383,"children":3384},{"style":503},[3385],{"type":399,"value":632},{"type":393,"tag":464,"props":3387,"children":3389},{"id":3388},"️-smart-file-loader",[3390],{"type":399,"value":3391},"🛠️ Smart File Loader",{"type":393,"tag":402,"props":3393,"children":3394},{},[3395],{"type":399,"value":3396},"Here's a comprehensive file loader that handles most problems:",{"type":393,"tag":476,"props":3398,"children":3399},{},[3400],{"type":393,"tag":480,"props":3401,"children":3403},{"className":482,"code":3402,"language":484,"meta":6,"style":6},"import pandas as pd\nimport os\n\ndef smart_file_loader(filename, **kwargs):\n    \"\"\"Smart file loader with multiple fallback strategies\"\"\"\n    \n    # Check if file exists\n    if not os.path.exists(filename):\n        print(f\"❌ File '{filename}' not found\")\n        return None\n    \n    # Get file extension\n    _, ext = os.path.splitext(filename.lower())\n    \n    # Try loading based on extension\n    try:\n        if ext == '.csv':\n            return try_csv_loading(filename, **kwargs)\n        elif ext in ['.xlsx', '.xls']:\n            return try_excel_loading(filename, **kwargs)\n        elif ext == '.json':\n            return try_json_loading(filename, **kwargs)\n        else:\n            print(f\"❌ Unsupported file type: {ext}\")\n            return None\n            \n    except Exception as e:\n        print(f\"❌ Failed to load {filename}: {e}\")\n        return None\n\ndef try_csv_loading(filename, **kwargs):\n    \"\"\"Try CSV with different parameters\"\"\"\n    \n    # Try basic loading first\n    try:\n        df = pd.read_csv(filename, **kwargs)\n        print(f\"✅ CSV loaded: {df.shape}\")\n        return df\n    except:\n        pass\n    \n    # Try different separators\n    for sep in [';', '\\t', '|']:\n        try:\n            df = pd.read_csv(filename, sep=sep, **kwargs)\n            print(f\"✅ CSV loaded with separator '{sep}': {df.shape}\")\n            return df\n        except:\n            continue\n    \n    # Try different encodings\n    for encoding in ['latin-1', 'cp1252']:\n        try:\n            df = pd.read_csv(filename, encoding=encoding, **kwargs)\n            print(f\"✅ CSV loaded with encoding '{encoding}': {df.shape}\")\n            return df\n        except:\n            continue\n    \n    print(\"❌ All CSV loading strategies failed\")\n    return None\n\ndef try_excel_loading(filename, **kwargs):\n    \"\"\"Try Excel with different engines\"\"\"\n    engines = ['openpyxl', 'xlrd']\n    \n    for engine in engines:\n        try:\n            df = pd.read_excel(filename, engine=engine, **kwargs)\n            print(f\"✅ Excel loaded with engine '{engine}': {df.shape}\")\n            return df\n        except:\n            continue\n    \n    print(\"❌ All Excel loading strategies failed\")\n    return None\n\ndef try_json_loading(filename, **kwargs):\n    \"\"\"Try JSON loading\"\"\"\n    try:\n        df = pd.read_json(filename, **kwargs)\n        print(f\"✅ JSON loaded: {df.shape}\")\n        return df\n    except:\n        print(\"❌ JSON loading failed\")\n        return None\n\n# Test the smart loader\nsample_data = \"name,age\\nAlice,25\\nBob,30\"\nwith open('test_smart.csv', 'w') as f:\n    f.write(sample_data)\n\ndf = smart_file_loader('test_smart.csv')\nif df is not None:\n    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