diff --git a/07.Project 2 - Data Visualization/Interactive Visualization with Pandas.ipynb b/07.Project 2 - Data Visualization/Interactive Visualization with Pandas.ipynb
index a0816aa..784f28f 100644
--- a/07.Project 2 - Data Visualization/Interactive Visualization with Pandas.ipynb
+++ b/07.Project 2 - Data Visualization/Interactive Visualization with Pandas.ipynb
@@ -2,26 +2,236 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "db3ee280",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
- "import cufflinks as cf\n",
- "from IPython.display import display, HTML\n",
- "\n",
- "cf.set_config_file(sharing='public', theme='ggplot', offline=True)"
+ "import plotly.express as px"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "5210cda7",
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
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+ " China | \n",
+ " Indonesia | \n",
+ " Brazil | \n",
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\n",
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+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# reading the csv file\n",
"df_population_raw = pd.read_csv('population_total.csv')\n",
@@ -47,13 +257,748 @@
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"source": [
- "df_pivot.iplot(kind='line', xTitle='Years', yTitle='Population', \n",
- " title='Population (1955-2020)')"
+ "fig = px.line(df_pivot, title='Population (1955-2020)',\n",
+ " labels={'value':'Population','year':'Years','variable':'Country'})\n",
+ "\n",
+ "#Updating the theme\n",
+ "fig.update_layout(template='ggplot2')\n",
+ "\n",
+ "#Displaying the lineplot\n",
+ "fig.show()"
]
},
{
@@ -66,7 +1011,7 @@
},
{
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- "execution_count": null,
+ "execution_count": 6,
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"source": [
- "df_pivot_2020.iplot(kind='bar', color='lightgreen',\n",
- " xTitle='Years', yTitle='Population')"
+ "fig = px.bar(df_pivot_2020,barmode='group',\n",
+ " labels={'values':'Population','country':'Countries'})\n",
+ "\n",
+ "#Display the single Bargraph\n",
+ "fig.show()\n"
]
},
{
@@ -98,7 +1898,7 @@
},
{
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- "execution_count": null,
+ "execution_count": 13,
"id": "b4e043a6",
"metadata": {},
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@@ -109,12 +1909,971 @@
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},
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@@ -127,7 +2886,7 @@
},
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@@ -138,22 +2897,924 @@
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\n",
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+ " 1.380004e+09 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2 | \n",
+ " China | \n",
+ " 1.439324e+09 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 3 | \n",
+ " Indonesia | \n",
+ " 2.735236e+08 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " Brazil | \n",
+ " 2.125594e+08 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "year index country 2020\n",
+ "0 0 United States 3.310027e+08\n",
+ "1 1 India 1.380004e+09\n",
+ "2 2 China 1.439324e+09\n",
+ "3 3 Indonesia 2.735236e+08\n",
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+ ]
+ },
+ "execution_count": 22,
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"source": [
- "df_pivot['United States'].iplot(kind='box', color='green',\n",
- " yTitle='Population')"
+ "#Single Box plot\n",
+ "\n",
+ "df_pivot_single_country = df_pivot[['India']]\n",
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+ "fig.show()"
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},
{
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"id": "828a47a5",
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"source": [
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},
{
@@ -197,13 +5656,951 @@
},
{
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"source": [
- "df_pivot[['United States', 'Indonesia']].iplot(kind='hist', xTitle='Population',\n",
- " bins=3)"
+ "fig = px.histogram(df_pivot,nbins=3,barmode='overlay')\n",
+ "\n",
+ "fig.show()"
]
},
{
@@ -216,18 +6613,962 @@
},
{
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+ "type": "surface"
+ }
+ ],
+ "table": [
+ {
+ "cells": {
+ "fill": {
+ "color": "#EBF0F8"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "header": {
+ "fill": {
+ "color": "#C8D4E3"
+ },
+ "line": {
+ "color": "white"
+ }
+ },
+ "type": "table"
+ }
+ ]
+ },
+ "layout": {
+ "annotationdefaults": {
+ "arrowcolor": "#2a3f5f",
+ "arrowhead": 0,
+ "arrowwidth": 1
+ },
+ "autotypenumbers": "strict",
+ "coloraxis": {
+ "colorbar": {
+ "outlinewidth": 0,
+ "ticks": ""
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+ },
+ "colorscale": {
+ "diverging": [
+ [
+ 0,
+ "#8e0152"
+ ],
+ [
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+ "#c51b7d"
+ ],
+ [
+ 0.2,
+ "#de77ae"
+ ],
+ [
+ 0.3,
+ "#f1b6da"
+ ],
+ [
+ 0.4,
+ "#fde0ef"
+ ],
+ [
+ 0.5,
+ "#f7f7f7"
+ ],
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+ "#e6f5d0"
+ ],
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+ "#b8e186"
+ ],
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+ "#7fbc41"
+ ],
+ [
+ 0.9,
+ "#4d9221"
+ ],
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+ 1,
+ "#276419"
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+ [
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+ "#0d0887"
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+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Years"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Population"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "df_pivot.iplot(kind='scatter', mode='markers')"
+ "px.scatter(df_pivot,labels={'value':'Population','year':'Years'})"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df04b60b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "base",
"language": "python",
"name": "python3"
},
@@ -241,7 +7582,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.13.5"
},
"toc": {
"base_numbering": 1,