diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..f17aa9a 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 160, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 161, "metadata": {}, "outputs": [], "source": [ @@ -44,10 +44,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0 5.7\n", + "1 75.2\n", + "2 74.4\n", + "3 84.0\n", + "4 66.5\n", + "5 66.3\n", + "6 55.8\n", + "7 75.7\n", + "8 29.1\n", + "9 43.7\n", + "dtype: float64" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_ = pd.Series(lst)\n", + "series_" + ] }, { "cell_type": "markdown", @@ -60,10 +84,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_[2]" + ] }, { "cell_type": "markdown", @@ -74,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 164, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +129,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Score_1Score_2Score_3Score_4Score_5
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DescriptionQuantityUnitPriceRevenue
0LUNCH BAG APPLE DESIGN11.651.65
1SET OF 60 VINTAGE LEAF CAKE CASES240.5513.20
2RIBBON REEL STRIPES DESIGN11.651.65
3WORLD WAR 2 GLIDERS ASSTD DESIGNS28800.18518.40
4PLAYING CARDS JUBILEE UNION JACK21.252.50
5POPCORN HOLDER70.855.95
6BOX OF VINTAGE ALPHABET BLOCKS111.9511.95
7PARTY BUNTING44.9519.80
8JAZZ HEARTS ADDRESS BOOK100.191.90
9SET OF 4 SANTA PLACE SETTINGS481.2560.00
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" + ], + "text/plain": [ + " Description Quantity UnitPrice Revenue\n", + "0 LUNCH BAG APPLE DESIGN 1 1.65 1.65\n", + "1 SET OF 60 VINTAGE LEAF CAKE CASES 24 0.55 13.20\n", + "2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65\n", + "3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.40\n", + "4 PLAYING CARDS JUBILEE UNION JACK 2 1.25 2.50\n", + "5 POPCORN HOLDER 7 0.85 5.95\n", + "6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n", + "7 PARTY BUNTING 4 4.95 19.80\n", + "8 JAZZ HEARTS ADDRESS BOOK 10 0.19 1.90\n", + "9 SET OF 4 SANTA PLACE SETTINGS 48 1.25 60.00" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] }, { "cell_type": "markdown", @@ -238,10 +822,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 176, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total quantity ordered is 2978, and the revenue generated is 637.0\n" + ] + } + ], + "source": [ + "print(f\"The total quantity ordered is {df['Quantity'].sum()}, and the revenue generated is {df['Revenue'].sum()}\")" + ] }, { "cell_type": "markdown", @@ -252,10 +846,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 177, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.77\n" + ] + } + ], + "source": [ + "print(df[\"UnitPrice\"].max() - df[\"UnitPrice\"].min())" + ] }, { "cell_type": "markdown", @@ -266,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +889,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 179, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1231610433.03.58.0010.72
2332211033.52.58.6710.80
3431410322.03.08.2100.65
4533011554.53.09.3410.90
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Chance of Admit0
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" + ], + "text/plain": [ + " 0\n", + "Serial No. 0\n", + "GRE Score 0\n", + "TOEFL Score 0\n", + "University Rating 0\n", + "SOP 0\n", + "LOR 0\n", + "CGPA 0\n", + "Research 0\n", + "Chance of Admit 0" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(admissions.isnull().sum()) #There is no missing value" + ] }, { "cell_type": "markdown", @@ -313,17 +1122,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 181, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "admissions.set_index(\"Serial No.\", inplace = True, drop = False)" + ] }, { "cell_type": "markdown", @@ -334,10 +1138,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 183, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.duplicated(subset=[\"GRE Score\",\"CGPA\" ]).sum()" + ] }, { "cell_type": "markdown", @@ -348,10 +1165,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 185, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CGPA 110\n", + "dtype: int64" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(admissions[\"CGPA\"] > 9).sum() #110 times this condition is True" + ] }, { "cell_type": "markdown", @@ -362,17 +1195,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 186, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.7313235294117648" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[(admissions[\"CGPA\"] > 9) | (admissions[\"SOP\"] < 3.5)][\"Chance of Admit\"].mean()" + ] }, { "cell_type": "markdown", @@ -384,10 +1223,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 187, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def func_TOEFL(x):\n", + " return True if x > 100 else False\n", + "\n", + "admissions[\"TOEFL\"] = admissions[\"TOEFL Score\"].apply(func_TOEFL)" + ] }, { "cell_type": "markdown", @@ -398,24 +1242,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 188, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "admissions.rename(columns = {\"TOEFL\": \"Decision\"}, inplace=True)" + ] }, { "cell_type": "markdown", @@ -427,17 +1259,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 191, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def func_SOP(x):\n", + " return 1 if x > 3 else 0\n", + "admissions[\"decision2\"] = admissions[\"SOP\"].apply(func_SOP)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ironhack", "language": "python", - "name": "python3" + "name": "ironhack" }, "language_info": { "codemirror_mode": { @@ -449,7 +1285,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "toc": { "base_numbering": "",