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February 23, 2024 02:14
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dataframe
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "61561d2f", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "9f7f4456", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "data = {\n", | |
| " \"month\": [1, 2, 3, 4, 5],\n", | |
| " \"weather\": [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n", | |
| "}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "a2253cdb", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "{'month': [1, 2, 3, 4, 5], 'weather': ['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']}\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(data)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "e6bab59d", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.DataFrame(data)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "id": "f698d8a3", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "0 1 rainy\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n", | |
| "3 4 windy\n", | |
| "4 5 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "id": "11423624", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "m = list(df['month'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "id": "29faf2d5", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[1, 2, 3, 4, 5]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "id": "0d98f586", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df['weather'])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "id": "6c5ce912", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "KeyError", | |
| "evalue": "'other'", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", | |
| "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", | |
| "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", | |
| "\u001b[1;31mKeyError\u001b[0m: 'other'", | |
| "\nThe above exception was the direct cause of the following exception:\n", | |
| "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", | |
| "Cell \u001b[1;32mIn[9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mother\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3809\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", | |
| "\u001b[1;31mKeyError\u001b[0m: 'other'" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df['other'])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "7c3b3f87", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[1, 'rainy']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df.loc[0])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "id": "77705ec6", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[2, 'sunny']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df.loc[1])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "id": "ee87618a", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[5, 'cloudy']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df.loc[4])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "id": "40901b18", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "KeyError", | |
| "evalue": "5", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:391\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 390\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m 392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", | |
| "\u001b[1;31mValueError\u001b[0m: 5 is not in range", | |
| "\nThe above exception was the direct cause of the following exception:\n", | |
| "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", | |
| "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1312\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1310\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m 1311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_label(key, axis\u001b[38;5;241m=\u001b[39maxis)\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1260\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 1258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m 1259\u001b[0m \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mxs(label, axis\u001b[38;5;241m=\u001b[39maxis)\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\generic.py:4056\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m 4054\u001b[0m new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m 4055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4056\u001b[0m loc \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m 4058\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m 4059\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n", | |
| "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:393\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m 392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 393\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 394\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[0;32m 395\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n", | |
| "\u001b[1;31mKeyError\u001b[0m: 5" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = list(df.loc[5])\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "id": "be445cbe", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "month 1\n", | |
| "weather rainy\n", | |
| "Name: 0, dtype: object\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[0]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "id": "d9e5f892", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "month 5\n", | |
| "weather cloudy\n", | |
| "Name: 4, dtype: object\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[4]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "id": "9a9b6efb", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[1:2]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "id": "08d8345e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "0 1 rainy\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n", | |
| "3 4 windy\n", | |
| "4 5 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[0:4]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "id": "ea9e9f00", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "3 4 windy\n", | |
| "4 5 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[3:20]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "id": "c9c3fd42", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "0 1 rainy\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "m = df.loc[-1:2]\n", | |
| "print(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "id": "16ccb19e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "a 1 rainy\n", | |
| "b 2 sunny\n", | |
| "c 3 cloudy\n", | |
| "d 4 windy\n", | |
| "e 5 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "idx = ['a', 'b', 'c', 'd', 'e']\n", | |
| "df = pd.DataFrame(data, index=idx)\n", | |
| "print(df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "id": "aabebd2c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "month 1\n", | |
| "weather rainy\n", | |
| "Name: a, dtype: object\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(df.loc['a'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "id": "cba818a4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "month 5\n", | |
| "weather cloudy\n", | |
| "Name: e, dtype: object\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(df.loc['e'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "id": "799e96ae", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "a 1 rainy\n", | |
| "b 2 sunny\n", | |
| "c 3 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(df.loc['a':'c'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "id": "6ccaee2e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1" | |
| ] | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.loc['a']['month']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "id": "1d5fd2c8", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'windy'" | |
| ] | |
| }, | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.loc['d']['weather']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "id": "d4a3ff61", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_csv(\"data_no_idx_sep_comma.csv\", sep=\",\", index=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "id": "7e34744e", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_csv(\"data_def_options.csv\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "id": "92aacef5", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_csv(\"data_sep_semicolon.csv\", sep=\";\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "id": "d845b8db", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_csv(\"data_sep_tab.csv\", sep=\"\\t\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "id": "ced2a4e8", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_csv(\"data_sep_space.csv\", sep=\" \")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "id": "4dc947c7", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.to_excel(\"data_no_idx.xlsx\", index=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "id": "8d4f1656", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "0 1 rainy\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n", | |
| "3 4 windy\n", | |
| "4 5 cloudy\n", | |
| "5 6 snowy\n", | |
| "6 7 snowy\n", | |
| "7 8 windy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "df2 = pd.read_excel(\"data_no_idx.xlsx\")\n", | |
| "print(df2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "id": "327be910", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " month weather\n", | |
| "0 1 rainy\n", | |
| "1 2 sunny\n", | |
| "2 3 cloudy\n", | |
| "3 4 windy\n", | |
| "4 5 cloudy\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "df3 = pd.read_csv(\"data_no_idx_sep_comma.csv\")\n", | |
| "print(df3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "id": "3222a376", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "SyntaxError", | |
| "evalue": "invalid syntax (3608999492.py, line 2)", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[1;36m Cell \u001b[1;32mIn[36], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m =\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "• What would be the best separation characters for CSV file?\n", | |
| "= \n", | |
| "For most cases in Jupyter notebooks, commas (,) are a good default choice. However, if your data contains commas within fields or you need wider compatibility, consider semicolons (;) or a custom delimiter. Remember to document your choice clearly for future reference and collaboration.\n", | |
| "Ultimately, the best separation character depends on your specific needs and context. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "id": "5255da9a", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "ename": "SyntaxError", | |
| "evalue": "invalid character '•' (U+2022) (2165916699.py, line 1)", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[1;36m Cell \u001b[1;32mIn[37], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m • Explain why you choose that answer.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid character '•' (U+2022)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "• Explain why you choose that answer.\n", | |
| "= \n", | |
| "cause its depends on my needs and context,and look out for the factors too" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "a6fac1d7", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.11.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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