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Run notebooks with updates and custom kernel

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This commit is contained in:
Alexander Hess 2024-07-15 10:33:13 +02:00
commit 3125c82096
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
13 changed files with 102 additions and 114 deletions
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48
01_scientific_stack/02_content_pandas.ipynb
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@ -32,12 +32,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pandas in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (1.3.3)\n",
"Requirement already satisfied: numpy>=1.17.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (1.21.1)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (2.8.2)\n",
"Requirement already satisfied: pytz>=2017.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (2021.3)\n",
"Requirement already satisfied: six>=1.5 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
"Requirement already satisfied: pandas in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (2.2.2)\n",
"Requirement already satisfied: numpy>=1.26.0 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2.0.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2024.1)\n",
"Requirement already satisfied: tzdata>=2022.7 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2024.1)\n",
"Requirement already satisfied: six>=1.5 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
]
}
],
@ -927,7 +927,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 694 entries, 192594 to 211519\n",
"Index: 694 entries, 192594 to 211519\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
@ -1882,7 +1882,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 694 entries, 192594 to 211519\n",
"Index: 694 entries, 192594 to 211519\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
@ -2006,7 +2006,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 694 entries, 192594 to 211519\n",
"Index: 694 entries, 192594 to 211519\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
@ -3189,6 +3189,7 @@
{
"data": {
"text/plain": [
"restaurant_id\n",
"1254 78\n",
"1207 47\n",
"1204 39\n",
@ -3199,7 +3200,7 @@
"1249 23\n",
"1242 19\n",
"1221 18\n",
"Name: restaurant_id, dtype: int64"
"Name: count, dtype: int64"
]
},
"execution_count": 35,
@ -3219,6 +3220,7 @@
{
"data": {
"text/plain": [
"customer_id\n",
"73919 14\n",
"10298 12\n",
"6037 8\n",
@ -3229,7 +3231,7 @@
"76838 3\n",
"75905 3\n",
"74791 3\n",
"Name: customer_id, dtype: int64"
"Name: count, dtype: int64"
]
},
"execution_count": 36,
@ -3258,7 +3260,7 @@
{
"data": {
"text/plain": [
"15924.78"
"np.float64(15924.78)"
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"execution_count": 37,
@ -3278,7 +3280,7 @@
{
"data": {
"text/plain": [
"885.0"
"np.float64(885.0)"
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},
"execution_count": 38,
@ -3301,7 +3303,7 @@
{
"data": {
"text/plain": [
"3.5"
"np.float64(3.5)"
]
},
"execution_count": 39,
@ -3321,7 +3323,7 @@
{
"data": {
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"83.7"
"np.float64(83.7)"
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"execution_count": 40,
@ -3341,7 +3343,7 @@
{
"data": {
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"12.5"
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"execution_count": 41,
@ -3364,7 +3366,7 @@
{
"data": {
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"60.0"
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"execution_count": 42,
@ -3387,7 +3389,7 @@
{
"data": {
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"22.94636887608069"
"np.float64(22.94636887608069)"
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},
"execution_count": 43,
@ -3407,7 +3409,7 @@
{
"data": {
"text/plain": [
"22.95"
"np.float64(22.95)"
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"execution_count": 44,
@ -3427,7 +3429,7 @@
{
"data": {
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"22.69"
"np.float64(22.69)"
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"execution_count": 45,
@ -3445,9 +3447,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "intro-to-data-science",
"language": "python",
"name": "python3"
"name": "intro-to-data-science"
},
"language_info": {
"codemirror_mode": {
@ -3459,7 +3461,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
"version": "3.12.4"
},
"toc": {
"base_numbering": 1,