{ "cells": [ { "cell_type": "markdown", "id": "8a968910", "metadata": {}, "source": [ "This notebook provides practice problems associated with lesson 2. It is divided into several parts:\n", "* **Part 1** - most approachable place to start. Practices syntax and concepts\n", "* **Part 2** - a step up from part 1. Integrates concepts from accross the lesson in more applied scenarios" ] }, { "cell_type": "markdown", "id": "e92a4669", "metadata": {}, "source": [ "# `pandas` Answers" ] }, { "cell_type": "markdown", "id": "c6bf45ba", "metadata": {}, "source": [ "## Part 1" ] }, { "cell_type": "code", "execution_count": 1, "id": "e58ed9cc", "metadata": {}, "outputs": [], "source": [ "# Import the libary. You only have to do this once per file.\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "709503d9", "metadata": {}, "source": [ "### `pandas` Data Structures" ] }, { "cell_type": "markdown", "id": "d0ca5447", "metadata": {}, "source": [ "1. Use `pd.Dataframe()` to convert the following dictionary into a pandas Dataframe. Assign it to a variable called `earthquake_df`." ] }, { "cell_type": "code", "execution_count": 2, "id": "3c60adca", "metadata": {}, "outputs": [], "source": [ "earthquakes_dict = {\n", " 'magnitude': [3.2, 2.6, 5.4, 6.0, 6.0, 4.5, 4.2], \n", " 'depth (km)': [6, 5, 3, 15, 14, 10, 8],\n", " 'region': ['California', 'Philippines', 'Philippines', 'Indonesia', 'California', 'California', 'California']\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "id": "6dc47357", "metadata": {}, "outputs": [], "source": [ "earthquake_df = pd.DataFrame(earthquakes_dict)" ] }, { "cell_type": "markdown", "id": "21d840b8", "metadata": {}, "source": [ "2. Print just the Depth column from the dataframe. What is the data type of that that subset of data?" ] }, { "cell_type": "code", "execution_count": 4, "id": "69d5958a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 6\n", "1 5\n", "2 3\n", "3 15\n", "4 14\n", "5 10\n", "6 8\n", "Name: depth (km), dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "earthquake_df['depth (km)']" ] }, { "cell_type": "code", "execution_count": null, "id": "1993820f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "34e20993", "metadata": {}, "source": [ "Check your answer using `type()`" ] }, { "cell_type": "code", "execution_count": 5, "id": "f35f1317", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example\n", "type(earthquake_df) # Put your answer to #2 in place of `earthquake_df`" ] }, { "cell_type": "markdown", "id": "aa5c67fa", "metadata": {}, "source": [ "### Accessing values in Dataframes" ] }, { "cell_type": "code", "execution_count": 6, "id": "581e6d87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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magnitudedepth (km)region
03.26California
12.65Philippines
25.43Philippines
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" ], "text/plain": [ " magnitude depth (km) region\n", "0 3.2 6 California\n", "1 2.6 5 Philippines\n", "2 5.4 3 Philippines\n", "3 6.0 15 Indonesia\n", "4 6.0 14 California\n", "5 4.5 10 California\n", "6 4.2 8 California" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "earthquake_df" ] }, { "cell_type": "markdown", "id": "090f4784", "metadata": {}, "source": [ "3. print out the first row in the earthquakes dataframe" ] }, { "cell_type": "code", "execution_count": null, "id": "5b124751", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "a1c01069", "metadata": {}, "source": [ "4. print out the 2nd-5th rows in the earthquakes dataframe" ] }, { "cell_type": "code", "execution_count": null, "id": "784e9772", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "06201d52", "metadata": {}, "source": [ "5. take the 2nd and 3rd rows of the earthquakes dataframe and assign them to a variables called `philippeans_only`" ] }, { "cell_type": "code", "execution_count": null, "id": "1e0aca0d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b146cdcb", "metadata": {}, "source": [ "6. What is the data type of the new `pilippeans_only` variable?" ] }, { "cell_type": "code", "execution_count": null, "id": "b55ea40b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "5c55eaa9", "metadata": {}, "source": [ "### Inspecting and Aggregations" ] }, { "cell_type": "markdown", "id": "3ef419af", "metadata": {}, "source": [ "7. Use `.info()` to look up the data type of each of the columns in the dataframe" ] }, { "cell_type": "code", "execution_count": 7, "id": "f7e30e26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 7 entries, 0 to 6\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 magnitude 7 non-null float64\n", " 1 depth (km) 7 non-null int64 \n", " 2 region 7 non-null object \n", "dtypes: float64(1), int64(1), object(1)\n", "memory usage: 296.0+ bytes\n" ] } ], "source": [ "earthquake_df.info()" ] }, { "cell_type": "code", "execution_count": 8, "id": "048a9721", "metadata": {}, "outputs": [], "source": [ "# magnitude -> float, depth -> int, region -> object" ] }, { "cell_type": "markdown", "id": "c827e6cd", "metadata": {}, "source": [ "_Note: An \"object\" data type isn't one we have talked about before because it is specific to pandas. Pandas uses \"object\" as a datatype that is usually strings, but also includes any kind of mixed datatypes. Generally I just consider \"object\" datatype in pandas to mean \"string\", since that is the most common case._" ] }, { "cell_type": "markdown", "id": "1b73e14e", "metadata": {}, "source": [ "8. What is:\n", "\n", "* the strongest earthquake that is listed in this dataset (highest magnitude)?\n", "* the mean depth of these earthquakes?" ] }, { "cell_type": "code", "execution_count": 9, "id": "2ee55092", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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magnitudedepth (km)
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mean4.5571438.714286
std1.3339884.535574
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" ], "text/plain": [ " magnitude depth (km)\n", "count 7.000000 7.000000\n", "mean 4.557143 8.714286\n", "std 1.333988 4.535574\n", "min 2.600000 3.000000\n", "25% 3.700000 5.500000\n", "50% 4.500000 8.000000\n", "75% 5.700000 12.000000\n", "max 6.000000 15.000000" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "earthquake_df.describe()" ] }, { "cell_type": "markdown", "id": "da67240e", "metadata": {}, "source": [ "9. Googling practice: Find the number of earthquakes that each of the regions in the dataframe had. \n", "\n", "In other words answer the question, \"how many earthquakes occured in California?\", \"how many earthquakes occured in the phillipeans?\", etc." ] }, { "cell_type": "code", "execution_count": 10, "id": "b81689d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "California 4\n", "Philippines 2\n", "Indonesia 1\n", "Name: region, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "earthquake_df['region'].value_counts()" ] }, { "cell_type": "markdown", "id": "ac81b939", "metadata": {}, "source": [ "\n", "If you're looking for a place to start googling, try the phrase: \"pandas dataframe number of occurances of a value\"." ] }, { "cell_type": "markdown", "id": "1c0460ee", "metadata": {}, "source": [ "_**Hint** Check out [this](https://www.marsja.se/pandas-count-occurrences-in-column-unique-values/) piece of the internet_" ] }, { "cell_type": "markdown", "id": "4e229f5c", "metadata": {}, "source": [ "## Filepaths" ] }, { "cell_type": "markdown", "id": "56957969", "metadata": {}, "source": [ "You have been given a laminated piece of paper with the following file structure on it. This file structure is an abbreviated version of the file structure of the SARP laptops. `...` represents additional folder contents which are not shown in this diagram.\n", "\n", "![Folder structure](../images/SARP_computer_structure.jpg)\n" ] }, { "cell_type": "markdown", "id": "a500402a", "metadata": {}, "source": [ "### Absolute Filepaths\n", "1. `geopandas.ipynb`\n", "2. `shrubland_change_Jan2022-Dec2006.mp4`\n", "3. `dramatic_chipmunk.gif`\n", "\n", "### Relative Filepaths\n", "1. `example_code.py` -> `dramatic_chipmunk.gif`\n", "1. `geopandas.ipynb` -> `CAcountymap.geojson`\n", "1. `extract_features.ipynb` -> `aviris_f1806t01p00r02_img`\n", "3. `geopandas.ipynb` -> `aviris_f1806t01p00r02_img`\n", "2. `classify_shrublands.ipynb` -> `dramatic_chipmunk.gif`" ] }, { "cell_type": "markdown", "id": "f8768e32", "metadata": {}, "source": [ "### Absolute Filepaths\n", "1. `C:\\\\Users\\SARP\\Documents\\projects\\lessons\\geopandas.ipynb`\n", "2. `C:\\\\Users\\SARP\\Documents\\projects\\crop_detection\\visualizations\\shrubland_change_Jan2022-Dec2006.mp4`\n", "3. `C:\\\\Users\\SARP\\Desktop\\dramatic_chipmunk.gif`\n", "\n", "### Relative Filepaths\n", "1. from `example_code.py`: `.\\dramatic_chipmunk.gif`\n", "1. from `geopandas.ipynb`: `.\\data\\CAcountymap.geojson`\n", "1. from `extract_features.ipynb`: `..\\data\\aviris_f1806t01p00r02_img`\n", "3. from `geopandas.ipynb`: `..\\crop_detection\\data\\aviris_f1806t01p00r02_img`\n", "2. from `classify_shrublands.ipynb`: `..\\..\\..\\..\\Desktop\\dramatic_chipmunk.gif`\n", "\n" ] }, { "cell_type": "markdown", "id": "86213c87", "metadata": {}, "source": [ "## Part 2" ] }, { "cell_type": "markdown", "id": "0c14657f", "metadata": {}, "source": [ "Please run the following cells to have access to the sample USGS data before you start the practice problems." ] }, { "cell_type": "code", "execution_count": 11, "id": "03d68229", "metadata": {}, "outputs": [], "source": [ "# Read in the data\n", "water_vars = pd.read_csv('./data/englewood_3_12_21_usgs_water.tsv', sep='\\t', skiprows=30)\n", "# There are a lot of variables here, so let's shorten our dataframe to a few variables\n", "water_vars = water_vars[['datetime', '210920_00060', '210922_00010', '210924_00300', '210925_00400']]\n", "# Get rid of the first row of hard-coded datatype info\n", "water_vars = water_vars.drop(0)\n", "# Rename the columns from their USGS codes to more human-readible names\n", "name_codes = {'210920_00060': 'discharge','210922_00010': 'temperature (C)', '210924_00300': 'dissolved oxygen', '210925_00400': 'pH'}\n", "water_vars = water_vars.rename(columns=name_codes)\n", "# Convert all numeric columns to the proper datatype\n", "numeric_cols = water_vars.columns.drop('datetime')\n", "water_vars[numeric_cols] = water_vars[numeric_cols].apply(pd.to_numeric)" ] }, { "cell_type": "code", "execution_count": 12, "id": "34dce91d", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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datetimedischargetemperature (C)dissolved oxygenpH
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146 rows × 5 columns

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" ], "text/plain": [ " datetime discharge temperature (C) dissolved oxygen pH\n", "1 2021-03-12 00:00 44.5 8.1 8.3 8.1\n", "2 2021-03-12 00:15 44.5 8.1 8.2 8.1\n", "3 2021-03-12 00:30 44.5 8.1 8.2 8.1\n", "4 2021-03-12 00:45 44.5 8.1 8.1 8.1\n", "5 2021-03-12 01:00 44.5 8.1 8.1 8.1\n", ".. ... ... ... ... ...\n", "142 2021-03-13 11:15 42.6 6.7 9.8 7.9\n", "143 2021-03-13 11:30 42.6 6.7 9.9 7.9\n", "144 2021-03-13 11:45 42.6 6.7 10.2 7.9\n", "145 2021-03-13 12:00 46.5 6.7 10.3 7.9\n", "146 2021-03-13 12:15 NaN 6.6 10.3 7.9\n", "\n", "[146 rows x 5 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "water_vars" ] }, { "cell_type": "markdown", "id": "862c9564", "metadata": {}, "source": [ "### Question 1\n", "\n", "**A)** Return the mean of all the columns" ] }, { "cell_type": "code", "execution_count": null, "id": "5a0ed395", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "153bbfd6", "metadata": {}, "source": [ "**B)** Return the mean of just the dissolved oxygen column" ] }, { "cell_type": "code", "execution_count": null, "id": "a4409288", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "be942dba", "metadata": {}, "source": [ "**C**) Return the total discharge from the full dataframe" ] }, { "cell_type": "code", "execution_count": null, "id": "9cd1c41f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "52d1eff9", "metadata": {}, "source": [ "**D)** Return the mean values of all the columns for the first 15 rows" ] }, { "cell_type": "code", "execution_count": null, "id": "7d9d4379", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "a3dec889", "metadata": {}, "source": [ "#### Question 2\n", "\n", "Just like with dictionaries, the syntax for viewing a key (in dictionary)/column (in dataframe) is very similar to the syntax for creating a new key/column." ] }, { "cell_type": "code", "execution_count": 13, "id": "6123a3d7", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "1 8.1\n", "2 8.1\n", "3 8.1\n", "4 8.1\n", "5 8.1\n", " ... \n", "142 6.7\n", "143 6.7\n", "144 6.7\n", "145 6.7\n", "146 6.6\n", "Name: temperature (C), Length: 146, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example: View a column\n", "water_vars['temperature (C)']" ] }, { "cell_type": "code", "execution_count": 14, "id": "0c067e73", "metadata": {}, "outputs": [], "source": [ "# Example: Assign a column (Set all the values to 6)\n", "water_vars['temperature'] = 6" ] }, { "cell_type": "markdown", "id": "7ae2dcb3", "metadata": {}, "source": [ "Create a new temperature column which is the the temperature of the water in farenheit. Calculate the farenheit temperature using the celsius temperature in the `temperature (C)` column." ] }, { "cell_type": "code", "execution_count": null, "id": "c363b918", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "d1547c93", "metadata": {}, "source": [ "#### Question 3\n", "\n", "NaN values are an inevitable part of life when working with real data and its important to always be aware of them." ] }, { "cell_type": "markdown", "id": "0bd72aca", "metadata": {}, "source": [ "**A)** Use the function `.isnull()` to create a table full off Boolean values, indicating if that value is NaN or not\n", "\n", "_[`.isnull()` doc page](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isnull.html?highlight=isnull#pandas.DataFrame.isnull)_" ] }, { "cell_type": "code", "execution_count": 15, "id": "a70cb932", "metadata": {}, "outputs": [], "source": [ "# Run this line to make sure you will be able to see more rows in the output\n", "pd.set_option('display.min_rows', 40)" ] }, { "cell_type": "code", "execution_count": 16, "id": "b3d26c53", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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datetimedischargetemperature (C)dissolved oxygenpHtemperature
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" ], "text/plain": [ " datetime discharge temperature (C) dissolved oxygen pH \\\n", "1 False False False False False \n", "2 False False False False False \n", "3 False False False False False \n", "4 False False False False False \n", "5 False False False False False \n", "6 False False False False False \n", "7 False False False False False \n", "8 False False False False False \n", "9 False False False False False \n", "10 False False False False False \n", "11 False False False False False \n", "12 False False False False False \n", "13 False False False False False \n", "14 False False False False False \n", "15 False False False False False \n", "16 False False False False False \n", "17 False False False False False \n", "18 False False True False False \n", "19 False False False False False \n", "20 False False False False False \n", ".. ... ... ... ... ... \n", "127 False False False False False \n", "128 False False False False False \n", "129 False False False False False \n", "130 False False False False False \n", "131 False False False False False \n", "132 False False False False False \n", "133 False False False False False \n", "134 False False False False False \n", "135 False False False False False \n", "136 False False False False False \n", "137 False False False False False \n", "138 False False False False False \n", "139 False False False False False \n", "140 False False False False False \n", "141 False False False False False \n", "142 False False False False False \n", "143 False False False False False \n", "144 False False False False False \n", "145 False False False False False \n", "146 False True False False False \n", "\n", " temperature \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "5 False \n", "6 False \n", "7 False \n", "8 False \n", "9 False \n", "10 False \n", "11 False \n", "12 False \n", "13 False \n", "14 False \n", "15 False \n", "16 False \n", "17 False \n", "18 False \n", "19 False \n", "20 False \n", ".. ... \n", "127 False \n", "128 False \n", "129 False \n", "130 False \n", "131 False \n", "132 False \n", "133 False \n", "134 False \n", "135 False \n", "136 False \n", "137 False \n", "138 False \n", "139 False \n", "140 False \n", "141 False \n", "142 False \n", "143 False \n", "144 False \n", "145 False \n", "146 False \n", "\n", "[146 rows x 6 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "water_vars.isnull()" ] }, { "cell_type": "code", "execution_count": 17, "id": "7ff09c1a", "metadata": {}, "outputs": [], "source": [ "# Run this line after this problem if you'd like to reset your output display\n", "pd.set_option('display.min_rows', 10)" ] }, { "cell_type": "markdown", "id": "e8cdf683", "metadata": {}, "source": [ "**B)** Use the [`.dropna()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) method to replace all of the columns that have any NaN values. (_That will require an argument in the function._)" ] }, { "cell_type": "code", "execution_count": null, "id": "78bf9590", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "a7969a74", "metadata": {}, "source": [ "**C)** Drop any rows that have NaN values. Assign that dataframe to a new variable called `water_vars_nonan`." ] }, { "cell_type": "code", "execution_count": null, "id": "3e8136d0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "1c81b666", "metadata": {}, "source": [ "**D)** Replace all of the NaN values in this dataframe with a different value to indicate NaN: -999." ] }, { "cell_type": "code", "execution_count": null, "id": "5ab88b17", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "1915c83e", "metadata": {}, "source": [ "_Google hint: try \"pandas dataframe replace null values\"_\n", "\n", "_Stronger hint: [here's a page](https://www.geeksforgeeks.org/python-pandas-dataframe-fillna-to-replace-null-values-in-dataframe/)_" ] }, { "cell_type": "markdown", "id": "d5ad0c92", "metadata": {}, "source": [ "(After completing this problem please re-run the original data import cells at the top of Part 2 to set your data back to using NaNs.)" ] }, { "cell_type": "markdown", "id": "98133ecf", "metadata": {}, "source": [ "#### Question 4\n", "\n", "Sort the rows in the dataframe by discharge, with the largest discharge on the top (descending order). " ] }, { "cell_type": "code", "execution_count": null, "id": "ab714b3b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 18, "id": "91f10b2b", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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datetimedischargetemperature (C)dissolved oxygenpHtemperature
302021-03-12 07:1548.56.98.27.96
262021-03-12 06:1548.57.18.18.06
232021-03-12 05:3048.57.38.18.06
242021-03-12 05:4548.57.38.18.06
342021-03-12 08:1548.56.78.47.96
.....................
622021-03-12 15:1539.08.413.08.26
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1462021-03-13 12:15NaN6.610.37.96
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146 rows × 6 columns

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" ], "text/plain": [ " datetime discharge temperature (C) dissolved oxygen pH \\\n", "30 2021-03-12 07:15 48.5 6.9 8.2 7.9 \n", "26 2021-03-12 06:15 48.5 7.1 8.1 8.0 \n", "23 2021-03-12 05:30 48.5 7.3 8.1 8.0 \n", "24 2021-03-12 05:45 48.5 7.3 8.1 8.0 \n", "34 2021-03-12 08:15 48.5 6.7 8.4 7.9 \n", ".. ... ... ... ... ... \n", "62 2021-03-12 15:15 39.0 8.4 13.0 8.2 \n", "60 2021-03-12 14:45 39.0 8.3 12.8 8.2 \n", "59 2021-03-12 14:30 39.0 8.1 12.7 8.2 \n", "61 2021-03-12 15:00 39.0 8.4 12.9 8.2 \n", "146 2021-03-13 12:15 NaN 6.6 10.3 7.9 \n", "\n", " temperature \n", "30 6 \n", "26 6 \n", "23 6 \n", "24 6 \n", "34 6 \n", ".. ... \n", "62 6 \n", "60 6 \n", "59 6 \n", "61 6 \n", "146 6 \n", "\n", "[146 rows x 6 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "water_vars.sort_values(by='discharge', ascending=False)" ] }, { "cell_type": "markdown", "id": "efa5efc2", "metadata": {}, "source": [ "_Google support: \"pandas sort values\" search. Or, [this stackoverflow article](https://stackoverflow.com/questions/37787698/how-to-sort-pandas-dataframe-from-one-column) would be helpful, the second answer in particular._" ] }, { "cell_type": "code", "execution_count": null, "id": "a7833102", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b1d0abaf", "metadata": {}, "source": [ "#### Question 5" ] }, { "cell_type": "markdown", "id": "42dd127d", "metadata": {}, "source": [ "Before doing question 5 run the following lines of code. This will format our data a little nicer for this problem." ] }, { "cell_type": "code", "execution_count": 19, "id": "285d24b0", "metadata": {}, "outputs": [], "source": [ "# Set a new index. Instead of integers 0+, use the datetime instead\n", "water_vars = water_vars.set_index(pd.to_datetime(water_vars['datetime']))\n", "# Drop the old datetime column\n", "water_vars = water_vars.drop(columns='datetime')" ] }, { "cell_type": "markdown", "id": "4a27bf15", "metadata": {}, "source": [ "**A)** While there isn't a lot of control in it, we can use the dataframe method `.plot()` to make quick plots of our data. This isn't the way you would make plots for presentations, but it can still be useful sometimes to help us get a sense of our data.\n", "\n", "Try ising the `.plot()` method on the dataframe to make a quick plot. (Example: `forecast.plot()`, where `forecast` is the dataframe)" ] }, { "cell_type": "code", "execution_count": 20, "id": "f6850385", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "water_vars.plot()" ] }, { "cell_type": "markdown", "id": "4cca57c7", "metadata": {}, "source": [ "**B)** `.plot()` also works on a single Series. Try making a plot for just the disolved oxygen variable." ] }, { "cell_type": "code", "execution_count": null, "id": "f0b59c17", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "id": "fee2fbd1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "water_vars['dissolved oxygen'].plot()" ] }, { "cell_type": "markdown", "id": "0865f708", "metadata": {}, "source": [ "**C)** Looking at the first plot of all the variables we can see that discharge values are so high it shifts the graph so the other variables can't be seen as well.\n", "\n", "Create a plot that shows all the variables except for discharge. You can use the following line of code, which I used in the data cleaning lines for this problem, as a guide for dropping a single column from a dataframe:\n", "\n", "```\n", "# Droppping a single column, \"datetime\" from the water_vars dataframe\n", "water_vars.drop(columns='datetime')\n", "```" ] }, { "cell_type": "code", "execution_count": 22, "id": "30c727a9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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