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sloanlance/pandas-read-csv-with-other-encodings.ipynb

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pandas-read-csv-with-other-encodings.ipynb
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"<a href=\"https://colab.research.google.com/gist/sloanlance/2624a36e9c3eacc67921c02eb6c9a6f5/pandas-read-csv-with-other-encodings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"source": [
"I was given a CSV file produced using Microsoft Excel on a Windows computer. When I tried to use Pandas' `read_csv()` to work with the file, I received exceptions like the following…\n",
"\n",
"```\n",
"UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 𝒏: invalid start byte\n",
"```\n",
"\n",
"Looking into the data, I saw the error was referring to the special character in \"O�Neil\". A web search for \"apostrophe 92\" led me to information that code point 92 (hex) in the \"windows-1252\" encoding is equivalent to Unicode code point 2019 (hex), which is \"RIGHT SINGLE QUOTATION MARK\".\n",
"\n",
"I came up with the solution of reading the CSV file with various encodings until one of them works. This takes some experimentation, because the data in the file can be decoded with multiple encoding styles, but not all of them will result in the intended characters."
],
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{
"cell_type": "code",
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"# An excerpt of a CSV file created by Microsoft Excel on a Windows computer.\n",
"with open('citations.csv', 'wb') as csvFile:\n",
" csvFile.write((\n",
" b'Topic,Item\\r\\n'\n",
" b'\"Zhang, Bob\",Appointed James O\\x92Neil Prof. of Empirical Sciences\\r\\n'\n",
" b'James O\\x92Neil Prof. of Empirical Sciences,Bob Zhang appointed\\r\\n'\n",
" b'\"Abdul, Su\\x92ad\",Scholarly activity\\r\\n'))"
],
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" Topic \\\n",
"0 Zhang, Bob \n",
"1 James O’Neil Prof. of Empirical Sciences \n",
"2 Abdul, Su’ad \n",
"\n",
" Item \n",
"0 Appointed James O’Neil Prof. of Empirical Scie... \n",
"1 Bob Zhang appointed \n",
"2 Scholarly activity "
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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"</div>\n",
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]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"from typing import List, Optional, Tuple\n",
"import pandas as pd\n",
"\n",
"UTF_8 = 'utf-8'\n",
"WINDOWS_1252 = 'windows-1252'\n",
"\n",
"def readCsvMultiEncoding(filename: str, encodings: List[str]) -> \\\n",
" Tuple[pd.DataFrame, str]:\n",
" \"\"\"\n",
" Try reading a CSV using several encodings until one works.\n",
" \"\"\"\n",
"\n",
" df: Optional[pd.DataFrame] = None\n",
" encoding: Optional[str] = None\n",
" lastException: Optional[Exception] = None\n",
"\n",
" for encoding in encodings:\n",
" lastException = None\n",
" try:\n",
" df = pd.read_csv(filename, encoding=encoding)\n",
" except UnicodeDecodeError as e:\n",
" lastException = e\n",
" continue\n",
"\n",
" if lastException:\n",
" raise lastException\n",
"\n",
" return df, encoding\n",
"\n",
"dfOriginal, encoding = readCsvMultiEncoding(\n",
" 'citations.csv', [UTF_8, WINDOWS_1252])\n",
"print(encoding)\n",
"dfOriginal"
]
},
{
"cell_type": "code",
"source": [
"# Example of changing content based on encoding type.\n",
"dfNew = pd.DataFrame()\n",
"\n",
"if encoding == WINDOWS_1252:\n",
" dfNew['Topic'] = dfOriginal['Topic'].str.replace('’', \"'\")\n",
" dfNew['Item'] = dfOriginal['Item'].str.replace('’', \"'\")\n",
"\n",
"dfNew"
],
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"text/plain": [
" Topic \\\n",
"0 Zhang, Bob \n",
"1 James O'Neil Prof. of Empirical Sciences \n",
"2 Abdul, Su'ad \n",
"\n",
" Item \n",
"0 Appointed James O'Neil Prof. of Empirical Scie... \n",
"1 Bob Zhang appointed \n",
"2 Scholarly activity "
],
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"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-178ffdef-268b-4151-aaaf-d2571bd3d58a button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 3
}
]
}
]
}
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