Skip to content

Instantly share code, notes, and snippets.

@manuelgeologo
Created August 4, 2020 22:08
Show Gist options
  • Save manuelgeologo/7e84c7a195778b9a0702a224fe4c043b to your computer and use it in GitHub Desktop.
Save manuelgeologo/7e84c7a195778b9a0702a224fe4c043b to your computer and use it in GitHub Desktop.
Created on Skills Network Labs
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Get to Know a Pandas Array </h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will use the dataframe <code>df</code> for the following:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df=pd.DataFrame({'a':[1,2,1],'b':[1,1,1]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) find the unique values in column <code> 'a' </code>:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) return a dataframe with only the rows where column <code> a </code> is less than two "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"<small>Copyright &copy; 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment