unit02_reading and selecting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit 2 - Reading, describing, and selecting data\n",
    "---\n",
    "\n",
    "1. [Reading files](#section1)\n",
    "2. [Describe the data](#section2)\n",
    "3. [Selecting columns](#section3)\n",
    "4. [Conditional selection](#section4)\n",
    "5. [Changing values on a slice](#section5)\n",
    "6. [loc and iloc](#section6)\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1. Reading files\n",
    "---\n",
    "\n",
    "<div>\n",
    "<img src=\"https://github.com/nlihin/data-analytics/blob/main/images/reading%20pandas.png?raw=true\" width=\"400\"/>\n",
    "<figcaption align=\"center\">\n",
    "    <small> Image generated by gencraft using ChatGPT text </small>\n",
    "</figcaption>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will read the file using pandas\\\n",
    "Pandas knows to read csv files, as while as other file types\\\n",
    "[More details](https://pandas.pydata.org/docs/user_guide/io.html#io-read-csv-table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will read data on [reported aircraft wildlife strike in the U.S](https://wildlife.faa.gov/search)\\\n",
    "I downloaded the file from the FAA and uploaded it to my Github. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/nlihin/data-analytics/main/datasets/aircraft%20wildlife%20strikes.csv'\n",
    "strike_df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the first few rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read_csv has about 30 different options. See the \n",
    "[documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
    "\n",
    "For example, sep='\\t' is used for tab delimited files and 'usecol' reads only specific columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Basic information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(strike_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(strike_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "view the shape of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "view basic information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df['Incident Year'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    "> What do you think that the `tail()` command does? Try it out!\n",
    ">\n",
    "> What happens if we just type `strike_df`, without a head or a tail?\n",
    ">\n",
    "> Find what kinds of `Flight Impact` are in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "A summary of the functions so far:\n",
    "\n",
    ">* `pd.read_csv` - Read data from a CSV file into a Pandas `DataFrame` object\n",
    ">* `.info()` - View basic infomation about rows, columns & data types\n",
    ">* `.columns` - Get the list of column names\n",
    ">* `.shape` - Get the number of rows & columns as a tuple\n",
    ">* `.head()`, `.tail()` - View the beginning/end of the file\n",
    ">* `.unique()` - view the unique entries in a series (not a dataframe) object\n",
    ">* `len` - dataframe length (number of rows)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='section2'></a>\n",
    "## 2. Describe the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe **numeric** data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a note on e: e stands for exponent of 10, and it's always followed by another number, which is the value of the exponent\n",
    "\n",
    "5e + 1 = 5 X 10^1 = 50\n",
    "\n",
    "6e + 2 = 6 X 10^2 = 600\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe **categorical** data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.describe(include = 'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[['Species Name']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count how many times each species is mentioned in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df['Species Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `index` and `values` to access the index and values of the series that returned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "A summary of the functions in this unit:\n",
    "\n",
    ">* `describe()` - View statistical information for numerical data\n",
    ">* `describe(include = 'object')` - for categorical data\n",
    ">* `value_counts()` - counts how many times a certain value appears in a column\n",
    ">* `index`, `values` - access the index and values of a series\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    "> Now you:\n",
    ">\n",
    "> What `Operator` has the most incidents?  ** Can you output ONLY the operator name?\n",
    ">\n",
    "> How many operators are there? **output ONLY a number\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='section3'></a>\n",
    "## 3. Selecting columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Selecting a single column:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**return a single column as a series:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.Airport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: using the `.` notation is possible only for columns whose names do not contain spaces or special characters.\\\n",
    "In all other cases, use []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[\"Flight Impact\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**return a single column as a dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[[\"Flight Impact\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Selecting a few columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[['Incident Year','Airport']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Select a specific cell: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.Airport[70000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    "> Now you:\n",
    ">\n",
    "> Select one column from the `strike_df` dataframe\n",
    ">\n",
    "> What is the difference between `strike_df[['Airport']]` and `strike_df['Airport']` and `strike_df.Airport` ?  Use `type` to find out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='section4'></a>\n",
    "## 4. Conditional selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our condition: only data on incidents involoving United Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df['Operator'] == 'UNITED AIRLINES'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates a series of true/false \n",
    "\n",
    "We can insert this into the dataframe to select only that task:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[strike_df['Operator'] == 'UNITED AIRLINES']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Selection using `query`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.query('Operator == \"UNITED AIRLINES\"').head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Select on more than one condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[(strike_df.Operator == 'UNITED AIRLINES') | (strike_df.Operator == 'DELTA AIR LINES')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[(strike_df.Operator == 'UNITED AIRLINES') & (strike_df['Incident Year'] == 2015)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    "> Select all the incidents of `Species Name`:MOURNING DOVE on 2014\n",
    ">\n",
    "> How many incidents of this type are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Selection using a variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the incident that occured in the maximum height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_height = strike_df['Height'].max()\n",
    "max_height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the incident details?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df[strike_df['Height'] == max_height]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    "> Select ONLY the Operator and Aircraft of the incident at the maximum height\n",
    ">\n",
    "> How many incidents occured for `Operator`: UNITED AIRLINES during `Visibility`: DAY?\n",
    ">\n",
    "> ** Select all incidents that did NOT occur in 2010\n",
    ">\n",
    "> ** What is the mean (average) speed at which incidents occured for `Operator`: UNITED AIRLINES during `Visibility`: DAY?\n",
    ">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "<a id='section5'></a>\n",
    "\n",
    "## 5. Changing values on a slice\n",
    "\n",
    "Looking at only part of a dataframe is called **slicing**, or a **view**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this is still the ORIGINAL dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we think we are creating a copy of the dataframe. But actually, we are just naming a slice of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df = strike_df[(strike_df.Operator == 'UNITED AIRLINES') & (strike_df['Incident Year'] == 2015)].copy()\n",
    "united_2015_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df['Airport'][124866]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change it to TEL AVIV\\\n",
    "We are warned that we are trying to change both our slice, and the original strike_df dataframe.\\\n",
    "Usually, if we are trying to change data is a slice, it mean we don't want to change data in the original dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df['Airport'] = \"TEL AVIV\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two ways to avoid the warning:  \n",
    "* use `loc` or `iloc`    \n",
    "* use `copy()` - will work in some cases (advanced: will not work with chained indexing). Takes up memory so use sparingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df.loc[124866,'Airport'] = \"LOD\"\n",
    "united_2015_df['Airport'][124866]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df['Airport'][124866]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works fine, no warnings. This won't change the original dataframe (which is a good things since if you planned to change it you would work on it and not on a slice)\\\n",
    "This will be the only way to do it from Pandas 3.0 onward\\\n",
    "[more explanations](https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#copy-on-write)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `copy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df = strike_df[(strike_df.Operator == 'UNITED AIRLINES') & (strike_df['Incident Year'] == 2015)].copy()\n",
    "united_2015_df['Airport'] = \"TEL AVIV\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`copy` doesn't work when chained indexing is involved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "united_2015_df['Airport'][124866] = \"TEL AVIV\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='section6'></a>\n",
    "## 6. `loc` and `iloc` \n",
    "\n",
    "One way to do that is iloc. \n",
    "\n",
    "`.iloc` - selects subsets of rows and columns by integer location only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#strike_df.iloc[[70000]]  #first row as a series\n",
    "#strike_df.iloc[[5,50,50000]]  #first row as a dataframe\n",
    "#strike_df.iloc[-1] #last row as a series\n",
    "strike_df.iloc[1:5,] #last row as a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The : operator \n",
    "\n",
    " - when used alone it means \"everything\"\n",
    "\n",
    "- also used to indicate a ***slice*** of values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.iloc[2:4] # second and third row\n",
    "#strike_df.iloc[[-1,2,22]] #a few specific rows\n",
    "\n",
    "# Columns:\n",
    "strike_df.iloc[:,0] # first column of data frame  \n",
    "strike_df.iloc[:,[1,2]] # second column of data frame  \n",
    "#strike_df.iloc[:,-1] # last column of data frame\n",
    "\n",
    "#Rows and columns\n",
    "#strike_df.iloc[0:5] # first five rows of dataframe\n",
    "strike_df.iloc[4:6, 0:2] # first two columns of data frame with all rows\n",
    "#strike_df.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if I want to select the 'daily_vaccinations' column, but I don't remember the column number?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `.loc`\n",
    "\n",
    "`.loc` - selects subsets of rows and columns by label only. Allowed inputs are:\n",
    "\n",
    "- A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).\n",
    "\n",
    "- A list or array of labels, e.g. ['a', 'b', 'c'].\n",
    "\n",
    "- A slice object with labels, e.g. 'a':'f'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.loc[2:4,['Airport','Incident Year']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets add the species name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strike_df.loc[0:3,['Airport','Incident Year','Species Name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Semantics are similar to iloc. But note:\n",
    "\n",
    "- `iloc` excludes the last element.  `df.iloc[0:1000]` will return entries 0...999\n",
    "- `loc`, includes the last element.  `df.loc[0:1000]` will return entries 0...1000\n",
    "\n",
    "you try it! What is the difference between:\n",
    "\n",
    "> strike_df.iloc[0:5]\n",
    "\n",
    "> strike_df.loc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <span style=\"color:blue\"> Exercise:</span>\n",
    ">Now you: \n",
    ">\n",
    ">what do you do to select:\n",
    ">\n",
    ">a. first five rows?\n",
    ">\n",
    ">b. first two columns, all rows?\n",
    ">\n",
    ">c. 1st and 3rd row and 2nd and 4th column?\n",
    ">\n",
    ">d(*). All columns but the first\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "A summary of the functions in this unit:\n",
    "\n",
    ">* `.iloc` - selects rows and columns by integer location\n",
    ">* `.loc` - selects rows and columns by label location\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: indexing operators as the ones working on dictionaries, will also work in pandas. But for more advanced operations, better get used to loc and iloc.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='section4'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}