From b30a9e14461986cea1e705e6d47418129263f524 Mon Sep 17 00:00:00 2001 From: gyoza1 Date: Mon, 18 Jul 2022 15:48:53 -0400 Subject: [PATCH] changed visualization from manually coded to automatic --- python_project_1.ipynb | 131 ++++++++++++++++++++++++++++++++++------- 1 file changed, 111 insertions(+), 20 deletions(-) diff --git a/python_project_1.ipynb b/python_project_1.ipynb index 5b1c8d8..93b6082 100644 --- a/python_project_1.ipynb +++ b/python_project_1.ipynb @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "897e335b", "metadata": {}, "outputs": [ @@ -151,7 +151,7 @@ "dtype: int64" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "e0ab351b", "metadata": {}, "outputs": [ @@ -465,7 +465,7 @@ "209786 0 0 " ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "c9a49215", "metadata": {}, "outputs": [], @@ -491,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "738ee993", "metadata": {}, "outputs": [], @@ -505,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "258073d0", "metadata": {}, "outputs": [ @@ -623,7 +623,7 @@ "4 s " ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "1f05cd97", "metadata": {}, "outputs": [ @@ -679,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "f86ea927", "metadata": {}, "outputs": [ @@ -1035,7 +1035,7 @@ "107115 44.4 " ] }, - "execution_count": 25, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1048,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "1cc0ab0a", "metadata": {}, "outputs": [ @@ -1062,7 +1062,7 @@ "Name: Mean Score, dtype: float64" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1075,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "8ad44a32", "metadata": {}, "outputs": [ @@ -1089,7 +1089,7 @@ "Name: Mean Score, dtype: float64" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1106,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "3b420509", "metadata": {}, "outputs": [ @@ -1120,7 +1120,7 @@ "Name: Mean Score, dtype: float64" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1137,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 17, "id": "e0e8e522", "metadata": {}, "outputs": [ @@ -1147,7 +1147,7 @@ "" ] }, - "execution_count": 57, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -1179,7 +1179,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, + "id": "207154ad", + "metadata": {}, + "outputs": [], + "source": [ + "# a more elegant way to create the dictionary\n", + "\n", + "test0 = df[df['School DBN'] == '02M605'].groupby(['Year'])['Mean Score'].mean()\n", + "test1 = df[(df['School DBN'] != '02M605')\n", + " & (df['School Level'] == 'High school')\n", + " & (df['School DBN'].str.contains('M'))\n", + " & (df['Regents Exam']).str.contains('English')\n", + " ].groupby(['Year'])['Mean Score'].mean()\n", + "test2 = df[(df['School DBN'] != '02M605')\n", + " & (df['School Level'] == 'High school')\n", + " & (~df['School DBN'].str.contains('M'))\n", + " & (df['Regents Exam']).str.contains('English')\n", + " ].groupby(['Year'])['Mean Score'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "19a88bc3", + "metadata": {}, + "outputs": [], + "source": [ + "# 3 dictionaries to 3 dataframes\n", + "\n", + "dict0 = pd.DataFrame.from_dict(test0)\n", + "dict1 = pd.DataFrame.from_dict(test1)\n", + "dict2 = pd.DataFrame.from_dict(test2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "7349a50d", + "metadata": {}, + "outputs": [], + "source": [ + "# join dataframes dict0 and dict1\n", + "\n", + "temp = dict0.join(dict1, lsuffix='_Humanities_Prep', rsuffix='_Other_Manhattan_Highs')\n", + "\n", + "# join temp and dict2\n", + "\n", + "newest_df = temp.join(dict2)\n", + "\n", + "# rename last column\n", + "\n", + "newest_df = newest_df.rename(columns = {'Mean Score': 'High Schools Other Boroughs'})" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "6d645276", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualization 2\n", + "\n", + "newest_df.plot(kind = 'bar', figsize = (10, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "id": "6749209e", "metadata": {}, "outputs": [],