{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stock exchange – visualising page data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from IPython.display import display, HTML\n",
"import altair as alt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('complete_page_list.csv', parse_dates=['date'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 27244\n",
"M 24538\n",
"N 20971\n",
"U 1\n",
"Name: session, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['session'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df['year'] = df['date'].dt.year"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_years = df['year'].value_counts().to_frame().reset_index()\n",
"df_years.columns = ['year', 'count']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Pages per year\n",
"\n",
"alt.Chart(df_years).mark_bar().encode(\n",
" x='year:N',\n",
" y='count:Q'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df_dates = pd.DataFrame(df['date'].unique())\n",
"df_dates['year'] = df_dates[0].dt.year\n",
"df_years_dates = df_dates['year'].value_counts().to_frame().reset_index()\n",
"df_years_dates.columns = ['year', 'count']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Days per year\n",
"\n",
"alt.Chart(df_years_dates).mark_bar().encode(\n",
" x='year:N',\n",
" y='count:Q'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"orders = {'M': 1, 'N': 2, 'A': 3, 'U': 4}\n",
"df_sessions = df.groupby(by='year')['session'].value_counts().to_frame().rename(columns={'session': 'count'}, level=0).reset_index()\n",
"df_sessions['order'] = df_sessions['session'].apply(lambda x: orders[x])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Days per year\n",
"\n",
"alt.Chart(df_sessions).mark_bar().encode(\n",
" x='year:N',\n",
" y='count:Q',\n",
" color='session:N',\n",
" order=alt.Order(\n",
" # Sort the segments of the bars by this field\n",
" 'order',\n",
" sort='ascending'\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"Created by [Tim Sherratt](https://timsherratt.org) for the [GLAM Workbench](https://glam-workbench.github.io/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}