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{
"cells": [
{
"cell_type": "markdown",
"id": "288dc3d6-2f59-4af4-b9a0-ac11110c95a4",
"metadata": {},
"source": [
"# a. What is CSV?"
]
},
{
"cell_type": "markdown",
"id": "8a29ef9f-d2b1-44ae-aa00-b7307dc1f1fa",
"metadata": {},
"source": [
"- CSV (Comma-Separated Values) is a simple and widely used file format for storing structured data.\n",
"- Each row in a CSV file represents a record, and fields within a record are separated by a delimiter (typically a comma, but can also be semicolons, tabs, etc.)."
]
},
{
"cell_type": "markdown",
"id": "aed4bbe7-49a7-44f7-a222-1dbc76b94b74",
"metadata": {},
"source": [
"## Advantages"
]
},
{
"cell_type": "markdown",
"id": "0908c962-52a0-481d-9c4a-734d0954aeb5",
"metadata": {},
"source": [
"- Lightweight and easy to create.\n",
"- Supported by almost all data tools and programming languages."
]
},
{
"cell_type": "markdown",
"id": "9a3c3937-cb91-411b-8606-16728aabbbc1",
"metadata": {},
"source": [
"## Common File Extensions"
]
},
{
"cell_type": "markdown",
"id": "41bf2a14-0cc1-458b-be33-62e9431a9b31",
"metadata": {},
"source": [
"- .csv\n",
"- .txt (sometimes used with a CSV structure)."
]
},
{
"cell_type": "markdown",
"id": "00250776-617f-49d9-88bb-e6cba943f599",
"metadata": {},
"source": [
"# b. How to Read CSV Files"
]
},
{
"cell_type": "markdown",
"id": "98989d08-8d4d-4a02-82b1-ba08757e71ff",
"metadata": {},
"source": [
"- Using Python, CSV files can be handled with libraries such as pandas or Python's built-in csv module."
]
},
{
"cell_type": "markdown",
"id": "6776fe4e-8155-47ff-99f4-ec26c916c45d",
"metadata": {},
"source": [
"## 1. Using pandas:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a508ae8d-3a3d-43f0-9453-11c87877b2b1",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Read a CSV file\n",
"df = pd.read_csv(\"file.csv\")\n",
"print(df.head())\n",
"\n",
"# Reading a CSV file with a custom delimiter\n",
"df = pd.read_csv(\"file.csv\", sep=\";\")"
]
},
{
"cell_type": "markdown",
"id": "7c3f7a6a-0c13-45f2-930b-2c5796985efd",
"metadata": {},
"source": [
"## 2. Using Python's Built-in csv Module:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a33ffb8b-88b6-4061-b816-00397f2b3a3e",
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"with open(\"file.csv\", \"r\") as file:\n",
" reader = csv.reader(file)\n",
" for row in reader:\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"id": "2a57c10b-51bf-4a4e-978a-51644964b856",
"metadata": {},
"source": [
"## 3.Reading Large CSV Files in Chunks:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a056573-3d16-400a-8ccd-a15d0398b454",
"metadata": {},
"outputs": [],
"source": [
"# Process large CSV files in smaller chunks\n",
"for chunk in pd.read_csv(\"large_file.csv\", chunksize=1000):\n",
" print(chunk.head())"
]
},
{
"cell_type": "markdown",
"id": "b52ebad6-0c64-4317-974a-3498f05feaea",
"metadata": {},
"source": [
"# c. Issues Encountered When Handling CSV Files"
]
},
{
"cell_type": "markdown",
"id": "8fb34287-7754-4170-8095-46c2a82db4ba",
"metadata": {},
"source": [
"1. Delimiter Issues:\n",
" - Not all CSV files use commas as delimiters. Some may use semicolons, tabs, or other characters.\n",
"2. Encoding Problems:\n",
" - Non-UTF-8 encodings may cause errors while reading files.\n",
" - Example: \"UnicodeDecodeError.\"\n",
"3. Missing or Inconsistent Data:\n",
" - Some fields may be empty, and column lengths may vary.\n",
"4. Header Issues:\n",
" - Files may lack headers or have duplicate/misaligned headers.\n",
"5. Large File Sizes:\n",
" - Processing very large CSV files can lead to memory issues."
]
},
{
"cell_type": "markdown",
"id": "67c01a56-9b7c-46ba-8a79-9586a244978c",
"metadata": {},
"source": [
"# d. How to Overcome These Issues"
]
},
{
"cell_type": "markdown",
"id": "45564d75-7870-45e1-8d53-e78ff71ff018",
"metadata": {},
"source": [
"1. Delimiter Issues:\n",
" - Specify the correct delimiter while reading:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36c282a6-cbdc-4a3e-933a-91080ea4dccc",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"file.csv\", sep=\";\")"
]
},
{
"cell_type": "markdown",
"id": "7b998672-5d7e-4a6a-8cc4-36b18446b9be",
"metadata": {},
"source": [
"2. Encoding Problems:\n",
" - Explicitly set the encoding:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2657d869-a303-4e03-bc07-b15f012f76e6",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"file.csv\", encoding=\"ISO-8859-1\")"
]
},
{
"cell_type": "markdown",
"id": "113e7e43-7031-4904-9e87-c9df4acefaff",
"metadata": {},
"source": [
"3. Handling Missing Data:\n",
" - Fill missing values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67ea80c7-7a86-4694-b6ff-a55ca27caad5",
"metadata": {},
"outputs": [],
"source": [
"df.fillna(\"Unknown\", inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "a6d7a40b-c495-4482-b006-767c14209bf2",
"metadata": {},
"source": [
"- Drop rows/columns with missing data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8191abd-6281-466e-aeba-5f8df351de2d",
"metadata": {},
"outputs": [],
"source": [
"df.dropna(inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "6542d341-d38f-4c59-a5ca-d2503bd35e51",
"metadata": {},
"source": [
"4. Header Issues:\n",
" - Manually assign headers:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f2ee8b5-c54d-4349-b473-a8c3d6230c38",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"file.csv\", header=None, names=[\"Col1\", \"Col2\", \"Col3\"])"
]
},
{
"cell_type": "markdown",
"id": "2461fa9d-02bb-4008-85d0-1cc47e412671",
"metadata": {},
"source": [
"5. Optimizing for Large Files:\n",
" - Use chunk processing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e684dc56-f980-4d37-affb-3d7fde7a99b0",
"metadata": {},
"outputs": [],
"source": [
"for chunk in pd.read_csv(\"file.csv\", chunksize=5000):\n",
" process(chunk)"
]
},
{
"cell_type": "markdown",
"id": "5a9677e2-e475-4829-9660-a2ec1674d221",
"metadata": {},
"source": [
"### Use lightweight libraries like dask or polars for very large files."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c378769c-56a9-4675-b988-e6b57eeed54e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe9e2b34-a679-4b8e-923a-f296f775a6a2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ece1968-048b-4337-a79e-3c9a7161231d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b940d8eb-c668-4553-9bb9-c1b8e39cf211",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a88eeae-cfdf-48bd-aa05-3b0c29ff25f0",
"metadata": {},
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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