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add colab link
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‎day_12/seaborn.ipynb‎

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@@ -5,14 +5,25 @@
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"colab": {
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"name": "seaborn.ipynb",
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"provenance": [],
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"collapsed_sections": []
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"collapsed_sections": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/gumdropsteve/intro_to_python/blob/main/day_12/seaborn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
@@ -24,7 +35,7 @@
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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],
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"execution_count": 157,
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"execution_count": null,
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"outputs": []
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},
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{
@@ -42,7 +53,7 @@
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"df = sns.load_dataset(\"titanic\")\n",
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"df.head()"
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],
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"execution_count": 158,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -209,7 +220,7 @@
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"# Look at dtypes and NaN \n",
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"df.info()"
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],
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"execution_count": 159,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
@@ -254,7 +265,7 @@
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"# How many NaN values in each column\n",
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"df.isnull().sum()"
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],
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"execution_count": 160,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -298,7 +309,7 @@
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"# Finding the mean of a column\n",
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"df[\"age\"].mean()"
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],
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"execution_count": 161,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -328,7 +339,7 @@
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"# Describe shows a lot of useful summary statistics\n",
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"df.describe()"
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],
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"execution_count": 162,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -469,7 +480,7 @@
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"# Look at NaN values in deck column\n",
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"df[\"deck\"]"
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],
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"execution_count": 163,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -514,7 +525,7 @@
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"\n",
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"df[[\"age\"]][:19].fillna(age_med)"
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],
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"execution_count": 165,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
@@ -673,7 +684,7 @@
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"# Backfill NaN values\n",
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"df[[\"age\"]][:19].fillna(method=\"bfill\")"
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],
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"execution_count": 166,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
@@ -832,7 +843,7 @@
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"# Forward fill NaN values\n",
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"df[[\"age\"]][:19].fillna(method=\"ffill\")"
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],
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"execution_count": 167,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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" df[\"age\"]\n",
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")"
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],
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"execution_count": 14,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"\n",
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"sns.histplot(df[\"age\"])"
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],
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"execution_count": 37,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"df.columns = col_title\n",
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"df.head()"
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],
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"execution_count": 168,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
@@ -1232,7 +1243,7 @@
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"# Create a new column and change the datatype\n",
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"df[\"Survived String\"] = df[\"Survived\"].astype(\"str\")"
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],
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"execution_count": 54,
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"execution_count": null,
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"outputs": []
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},
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{
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"g.despine(left=True)\n",
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"plt.title(\"Survival on the Titanic Separated by Class\");"
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],
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"execution_count": 169,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"sns.histplot(df[\"Age\"])\n",
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"sns.histplot(df[\"Fare\"])"
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],
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"execution_count": 94,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"sns.histplot(df[df[\"Class\"] == \"Second\"][\"Age\"])\n",
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"sns.histplot(df[df[\"Class\"] == \"Third\"][\"Age\"])"
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],
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"execution_count": 170,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"plt.legend(labels=[\"First Class\", \"Second Class\", \"Third Class\"])\n",
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"plt.title(\"KDE Plot of Fares grouped by class\");"
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],
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"execution_count": 180,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"plt.legend(labels=[\"First Class\", \"Second Class\", \"Third Class\"])\n",
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"plt.title(\"KDE Plot of Fares grouped by class\");"
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],
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"execution_count": 182,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"\n",
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"make_kde(df)"
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],
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"execution_count": 179,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"source": [
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"df[\"Class\"].value_counts()"
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],
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"execution_count": 95,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"sns.pairplot(df, hue=\"Class\")\n",
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"plt.title(\"Pairplot of Columns in Titanic Dataset\")"
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],
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"execution_count": 121,
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"execution_count": null,
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"outputs": [
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"output_type": "stream",
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"ax.set(ylabel=\"\")\n",
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"plt.title(\"Swarmplot\")"
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],
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"execution_count": 127,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"\n",
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"plt.title(\"Survival Violin Plot\");"
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],
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"execution_count": 130,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"# Find min value of fare\n",
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"df[\"Fare\"].min()"
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],
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"execution_count": 131,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"# Find max value of fare\n",
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"df[[\"Fare\"]].max()"
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],
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"execution_count": 135,
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"execution_count": null,
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"outputs": [
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"output_type": "execute_result",
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"# Whar methods can we use with a pandas dataframe object\n",
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"dir(df[[\"Fare\"]])"
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],
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"execution_count": 134,
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"execution_count": null,
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"outputs": [
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"output_type": "execute_result",
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" # multiple=\"fill\", # Look up what fill is doing with a kdeplot with multiple classes\n",
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")"
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],
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"execution_count": 155,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"# Seeing which class was represented the most in the data\n",
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"df[\"Class\"].value_counts().plot(kind=\"bar\", figsize=(10,10))"
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],
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"execution_count": 152,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",

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