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‎pandas.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" country capital area population\n",
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"0 Brazil Brasilia 8.516 200.40\n",
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"1 Russia Moscow 17.100 143.50\n",
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"2 India New Dehli 3.286 1252.00\n",
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"3 China Beijing 9.597 1357.00\n",
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"4 South Africa Pretoria 1.221 52.98\n"
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]
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}
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],
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"source": [
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"dict = {\"country\": [\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
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" \"capital\": [\"Brasilia\", \"Moscow\", \"New Dehli\", \"Beijing\", \"Pretoria\"],\n",
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" \"area\": [8.516, 17.10, 3.286, 9.597, 1.221],\n",
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" \"population\": [200.4, 143.5, 1252, 1357, 52.98] }\n",
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"\n",
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"import pandas as pd\n",
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"brics = pd.DataFrame(dict)\n",
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"print(brics)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" country capital area population\n",
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"v Brazil Brasilia 8.516 200.40\n",
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"i Russia Moscow 17.100 143.50\n",
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"s India New Dehli 3.286 1252.00\n",
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"h China Beijing 9.597 1357.00\n",
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"a South Africa Pretoria 1.221 52.98\n",
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"\n",
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" Unnamed: 0 cars_per_cap country drives_right\n",
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"0 US 809 United States True\n",
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"1 AUS 731 Australia False\n",
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"2 JAP 588 Japan False\n",
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"3 IN 18 India False\n",
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"4 RU 200 Russia True\n",
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"5 MOR 70 Morocco True\n",
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"6 EG 45 Egypt True\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"brics.index = [\"v\",\"i\",\"s\",\"h\",\"a\"]\n",
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"print(brics)\n",
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"print()\n",
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"\n",
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"\n",
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"#bost = pd.read_csv('C://Users//Tushar Raj Shrma//Desktop//cars.csv')\n",
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"# both are working\n",
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"by = pd.read_csv('C:\\\\Users\\\\Tushar Raj Shrma\\\\Desktop\\\\cars.csv')\n",
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"print(by)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" student add father\n",
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"0 ram v 77\n",
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"1 shyam j 27\n",
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"2 vansh o 56\n",
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"3 joker e 99 \n",
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"\n",
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"\n",
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" student add father\n",
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"A ram v 77\n",
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"L shyam j 27\n",
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"E vansh o 56\n",
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"X joker e 99\n",
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"\n",
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"A 77\n",
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"L 27\n",
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"E 56\n",
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"X 99\n",
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"Name: father, dtype: int64\n",
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"\n",
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" father student\n",
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"A 77 ram\n",
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"L 27 shyam\n",
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"E 56 vansh\n",
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"X 99 joker\n",
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"\n",
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"\n",
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" student add father\n",
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"L shyam j 27\n",
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"E vansh o 56\n",
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"X joker e 99\n",
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"\n",
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"student vansh\n",
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"add o\n",
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"father 56\n",
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"Name: E, dtype: object\n",
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"\n"
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]
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},
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{
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"ename": "KeyError",
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"evalue": "\"None of [Index(['ram', 'joker'], dtype='object')] are in the [index]\"",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-28-22b39840783a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#print(cars.loc[['AUS', 'EG']])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhasi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ram'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'joker'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1500\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1502\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1900\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot index with multidimensional key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1901\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1902\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1903\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1904\u001b[0m \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1203\u001b[0m \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1204\u001b[0m keyarr, indexer = self._get_listlike_indexer(key, axis,\n\u001b[1;32m-> 1205\u001b[1;33m raise_missing=False)\n\u001b[0m\u001b[0;32m 1206\u001b[0m return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},\n\u001b[0;32m 1207\u001b[0m copy=True, allow_dups=True)\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m 1159\u001b[0m self._validate_read_indexer(keyarr, indexer,\n\u001b[0;32m 1160\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1161\u001b[1;33m raise_missing=raise_missing)\n\u001b[0m\u001b[0;32m 1162\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m 1244\u001b[0m raise KeyError(\n\u001b[0;32m 1245\u001b[0m u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[1;32m-> 1246\u001b[1;33m key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[0;32m 1247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1248\u001b[0m \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['ram', 'joker'], dtype='object')] are in the [index]\""
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]
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}
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],
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"source": [
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"rule = {\"student\":[\"ram\",\"shyam\",\"vansh\",\"joker\"],\"add\":[\"v\",\"j\",\"o\",\"e\"],\"father\":[77,27,56,99]}\n",
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"import pandas as pd\n",
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"hasi = pd.DataFrame(rule)\n",
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"#in DataFrame (D and F must be in capital letter)\n",
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"print(hasi,\"\\n\")\n",
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"print()\n",
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"hasi.index = [\"A\",\"L\",\"E\",\"X\"]\n",
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"print(hasi)\n",
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"#hasi = pd.DataFrame(rule,index_col = 1)\n",
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"#print(hasi)\n",
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"print()\n",
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"\n",
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"print(hasi[\"father\"])\n",
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"print()\n",
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"print(hasi[[\"father\", \"student\"]])\n",
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"print()\n",
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"print()\n",
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"print(hasi[1:5])\n",
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"print()\n",
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"print(hasi.iloc[2]) # doubt\n",
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"print()\n",
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"#print(cars.loc[['AUS', 'EG']])\n",
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"print(hasi.loc[['ram','joker']]) # doubt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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