{"id":1787,"date":"2019-01-17T20:51:52","date_gmt":"2019-01-17T15:21:52","guid":{"rendered":"https:\/\/pynative.com\/?p=1787"},"modified":"2021-03-09T18:12:22","modified_gmt":"2021-03-09T12:42:22","slug":"python-pandas-exercise","status":"publish","type":"post","link":"https:\/\/pynative.com\/python-pandas-exercise\/","title":{"rendered":"Python Pandas Exercise"},"content":{"rendered":"\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"924\" height=\"319\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise.png\" alt=\"Python Pandas Exercise\" class=\"wp-image-1806\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise.png 924w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise-300x104.png 300w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise-768x265.png 768w\" sizes=\"(max-width: 924px) 100vw, 924px\" \/><\/figure>\n\n\n\n<p>This Pandas exercise project will help Python developers to learn and practice pandas. Pandas is an open-source, BSD-licensed Python library. Pandas is a handy and useful data-structure tool for analyzing large and complex data.<\/p>\n\n\n\n<p>Practice DataFrame, Data Selection, Group-By, Series, Sorting, Searching, statistics. Practice Data analysis using Pandas.<\/p>\n\n\n\n<p>In this exercise, we are using <strong>Automobile Dataset<\/strong> for data analysis. This Dataset has different characteristics of an auto such as body-style, wheel-base, engine-type, price, mileage, horsepower, etc.<\/p>\n\n\n\n<div class=\"wp-block-file\"><a href=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/Automobile_data.csv\">Automobile_data set<\/a><a href=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/Automobile_data.csv\" class=\"wp-block-file__button\" download>Download Automobile Dataset<\/a><\/div>\n\n\n\n<div class=\"wp-block-columns sectionpy has-background is-layout-flex wp-container-core-columns-is-layout-1 wp-block-columns-is-layout-flex\" style=\"background-color:#edf9f8\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Also Read<\/strong>:<\/p>\n\n\n\n<ul><li><a href=\"https:\/\/pynative.com\/python-pandas-dataframe\/\">Pandas DataFrame<\/a><\/li><\/ul>\n<\/div>\n<\/div>\n\n\n\n<p><strong>What included in this Pandas exercise?<\/strong><\/p>\n\n\n\n<ul><li>It contains 10 questions. The solution is provided for each question.<\/li><li>Each question includes a specific Pandas topic you need to learn. <\/li><\/ul>\n\n\n\n<p>When you complete each question, you get more familiar with data analysis using pandas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-1-from-the-given-dataset-print-the-first-and-last-five-rows\">Exercise 1: From the given dataset print the first and last five rows<\/h3>\n\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<div class=\"wp-block-image wp-image-1796 size-full\"><figure class=\"alignleft size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"803\" height=\"174\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_first_5_rows.png\" alt=\"Python Pandas printing first 5 rows\" class=\"wp-image-1796\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_first_5_rows.png 803w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_first_5_rows-300x65.png 300w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_first_5_rows-768x166.png 768w\" sizes=\"(max-width: 803px) 100vw, 803px\" \/><figcaption>Python Pandas printing first 5 rows<\/figcaption><\/figure><\/div>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1797\"><img loading=\"lazy\" decoding=\"async\" width=\"804\" height=\"166\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_last_5_rows.png\" alt=\"Python Pandas printing last 5 rows\" class=\"wp-image-1797\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_last_5_rows.png 804w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_last_5_rows-300x62.png 300w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_last_5_rows-768x159.png 768w\" sizes=\"(max-width: 804px) 100vw, 804px\" \/><figcaption>Python Pandas printing last 5 rows<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\">\n<p><strong>Print first five rows<\/strong><\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code1\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ndf.head(<span class=\"hljs-number\">5<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code1', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div>\n\n\n<p><strong>Print last five rows<\/strong><\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code2\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ndf.tail(<span class=\"hljs-number\">5<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code2', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-2-clean-the-dataset-and-update-the-csv-file\">Exercise 2: Clean the dataset and update the CSV file<\/h3>\n\n\n\n<p>Replace all column values which contain <code>?<\/code>, <code>n.a<\/code>, or <code>NaN<\/code>.<\/p>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code3\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-3\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">df = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>, na_values={\n<span class=\"hljs-string\">'price'<\/span>:&#91;<span class=\"hljs-string\">\"?\"<\/span>,<span class=\"hljs-string\">\"n.a\"<\/span>],\n<span class=\"hljs-string\">'stroke'<\/span>:&#91;<span class=\"hljs-string\">\"?\"<\/span>,<span class=\"hljs-string\">\"n.a\"<\/span>],\n<span class=\"hljs-string\">'horsepower'<\/span>:&#91;<span class=\"hljs-string\">\"?\"<\/span>,<span class=\"hljs-string\">\"n.a\"<\/span>],\n<span class=\"hljs-string\">'peak-rpm'<\/span>:&#91;<span class=\"hljs-string\">\"?\"<\/span>,<span class=\"hljs-string\">\"n.a\"<\/span>],\n<span class=\"hljs-string\">'average-mileage'<\/span>:&#91;<span class=\"hljs-string\">\"?\"<\/span>,<span class=\"hljs-string\">\"n.a\"<\/span>]})\n<span class=\"hljs-keyword\">print<\/span> (df)\n\ndf.to_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-3\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code3', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-3-find-the-most-expensive-car-company-name\">Exercise 3: Find the most expensive car company name<\/h3>\n\n\n\n<p>Print most expensive car&#8217;s company name and price.<\/p>\n\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone wp-image-1798 size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"221\" height=\"75\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_most_costly_car_name.png\" alt=\"Python Pandas printing most costly car name\" class=\"wp-image-1798\"\/><figcaption>Python Pandas printing most costly car name<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code4\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-4\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ndf = df &#91;&#91;<span class=\"hljs-string\">'company'<\/span>,<span class=\"hljs-string\">'price'<\/span>]]&#91;df.price==df&#91;<span class=\"hljs-string\">'price'<\/span>].max()]\ndf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-4\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code4', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-4-print-all-toyota-cars-details\">Exercise 4: Print All Toyota Cars details<\/h3>\n\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1793\"><img loading=\"lazy\" decoding=\"async\" width=\"781\" height=\"223\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_all_toyota_car_data.png\" alt=\"Python Pandas printing all Toyota cars data\" class=\"wp-image-1793\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_all_toyota_car_data.png 781w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_all_toyota_car_data-300x86.png 300w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_all_toyota_car_data-768x219.png 768w\" sizes=\"(max-width: 781px) 100vw, 781px\" \/><figcaption>Python Pandas printing all Toyota cars data<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code5\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-5\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ncar_Manufacturers = df.groupby(<span class=\"hljs-string\">'company'<\/span>)\ntoyotaDf = car_Manufacturers.get_group(<span class=\"hljs-string\">'toyota'<\/span>)\ntoyotaDf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-5\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code5', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-5-count-total-cars-per-company\">Exercise 5: Count total cars per company<\/h3>\n\n\n\n<p><strong>Expected Outcome<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1792\"><img loading=\"lazy\" decoding=\"async\" width=\"233\" height=\"303\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_count_total_cars_per_company.png\" alt=\"Python Pandas count total cars per company\" class=\"wp-image-1792\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_count_total_cars_per_company.png 233w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_count_total_cars_per_company-231x300.png 231w\" sizes=\"(max-width: 233px) 100vw, 233px\" \/><figcaption>Python Pandas count total cars per company<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code6\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-6\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ndf&#91;<span class=\"hljs-string\">'company'<\/span>].value_counts()<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-6\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code6', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-6-find-each-company-s-higesht-price-car\">Exercise 6: Find each company&#8217;s Higesht price car<\/h3>\n\n\n\n<p><strong>Expected Outcome<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"273\" height=\"479\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_each_companys_higesht_price_car.png\" alt=\"Python Pandas printing each company's highest price car\" class=\"wp-image-1795\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_each_companys_higesht_price_car.png 273w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_each_companys_higesht_price_car-171x300.png 171w\" sizes=\"(max-width: 273px) 100vw, 273px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code7\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-7\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ncar_Manufacturers = df.groupby(<span class=\"hljs-string\">'company'<\/span>)\npriceDf = car_Manufacturers&#91;<span class=\"hljs-string\">'company'<\/span>,<span class=\"hljs-string\">'price'<\/span>].max()\npriceDf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-7\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code7', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-7-find-the-average-mileage-of-each-car-making-company\">Exercise 7: Find the average mileage of each car making company<\/h3>\n\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1794\"><img loading=\"lazy\" decoding=\"async\" width=\"258\" height=\"487\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_average_mileage_of_each_car_making_company.png\" alt=\"Python Pandas printing average mileage of each car making company\" class=\"wp-image-1794\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_average_mileage_of_each_car_making_company.png 258w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_printing_average_mileage_of_each_car_making_company-159x300.png 159w\" sizes=\"(max-width: 258px) 100vw, 258px\" \/><figcaption>Python Pandas printing average mileage of each car making company<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code8\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-8\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ndf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ncar_Manufacturers = df.groupby(<span class=\"hljs-string\">'company'<\/span>)\nmileageDf = car_Manufacturers&#91;<span class=\"hljs-string\">'company'<\/span>,<span class=\"hljs-string\">'average-mileage'<\/span>].mean()\nmileageDf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-8\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code8', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-8-sort-all-cars-by-price-column\">Exercise 8: Sort all cars by Price column<\/h3>\n\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1799\"><img loading=\"lazy\" decoding=\"async\" width=\"812\" height=\"169\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_sort_all_cars_by_price_column.png\" alt=\"Python Pandas sort all cars by price column\" class=\"wp-image-1799\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_sort_all_cars_by_price_column.png 812w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_sort_all_cars_by_price_column-300x62.png 300w, https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_sort_all_cars_by_price_column-768x160.png 768w\" sizes=\"(max-width: 812px) 100vw, 812px\" \/><figcaption>Python Pandas sort all cars by price column<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code9\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-9\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncarsDf = pd.read_csv(<span class=\"hljs-string\">\"D:\\\\Python\\\\Articles\\\\pandas\\\\automobile-dataset\\\\Automobile_data.csv\"<\/span>)\ncarsDf = carsDf.sort_values(by=&#91;<span class=\"hljs-string\">'price'<\/span>, <span class=\"hljs-string\">'horsepower'<\/span>], ascending=<span class=\"hljs-literal\">False<\/span>)\ncarsDf.head(<span class=\"hljs-number\">5<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-9\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code9', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-9-concatenate-two-data-frames-using-the-following-conditions\">Exercise 9: Concatenate two data frames using the following conditions<\/h3>\n\n\n\n<p>Create two data frames using the following two <a href=\"https:\/\/pynative.com\/python-dictionaries\/\">dictionaries<\/a>.<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-10\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">GermanCars = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Ford'<\/span>, <span class=\"hljs-string\">'Mercedes'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">23845<\/span>, <span class=\"hljs-number\">171995<\/span>, <span class=\"hljs-number\">135925<\/span> , <span class=\"hljs-number\">71400<\/span>]}\njapaneseCars = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'Nissan'<\/span>, <span class=\"hljs-string\">'Mitsubishi '<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">29995<\/span>, <span class=\"hljs-number\">23600<\/span>, <span class=\"hljs-number\">61500<\/span> , <span class=\"hljs-number\">58900<\/span>]}<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-10\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1791\"><img loading=\"lazy\" decoding=\"async\" width=\"239\" height=\"257\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/pandas_concatenate_two_data_frames_and_create_key_for_each_data_frame.png\" alt=\"Python Pandas concatenate two data frames and create key for each data frame\" class=\"wp-image-1791\"\/><figcaption>Python Pandas concatenate two data frames and create a key for each data frame<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code10\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-11\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nGermanCars = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Ford'<\/span>, <span class=\"hljs-string\">'Mercedes'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">23845<\/span>, <span class=\"hljs-number\">171995<\/span>, <span class=\"hljs-number\">135925<\/span> , <span class=\"hljs-number\">71400<\/span>]}\ncarsDf1 = pd.DataFrame.from_dict(GermanCars)\n\njapaneseCars = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'Nissan'<\/span>, <span class=\"hljs-string\">'Mitsubishi '<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">29995<\/span>, <span class=\"hljs-number\">23600<\/span>, <span class=\"hljs-number\">61500<\/span> , <span class=\"hljs-number\">58900<\/span>]}\ncarsDf2 = pd.DataFrame.from_dict(japaneseCars)\n\ncarsDf = pd.concat(&#91;carsDf1, carsDf2], keys=&#91;<span class=\"hljs-string\">\"Germany\"<\/span>, <span class=\"hljs-string\">\"Japan\"<\/span>])\ncarsDf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-11\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code10', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-exercise-10-merge-two-data-frames-using-the-following-condition\">Exercise 10: Merge two data frames using the following condition<\/h3>\n\n\n\n<p>Create two data frames using the following two Dicts, Merge two data frames, and append the second data frame as a new column to the first data frame.<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-12\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">Car_Price = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">23845<\/span>, <span class=\"hljs-number\">17995<\/span>, <span class=\"hljs-number\">135925<\/span> , <span class=\"hljs-number\">71400<\/span>]}\ncar_Horsepower = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'horsepower'<\/span>: &#91;<span class=\"hljs-number\">141<\/span>, <span class=\"hljs-number\">80<\/span>, <span class=\"hljs-number\">182<\/span> , <span class=\"hljs-number\">160<\/span>]}<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-12\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong>Expected Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image alignnone size-full wp-image-1790\"><img loading=\"lazy\" decoding=\"async\" width=\"245\" height=\"146\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/merge_two_data_frames_and_append_new_data_frame_as_new-column.png\" alt=\"Python Pandas merge two data frames and append new data frame as new column\" class=\"wp-image-1790\"\/><figcaption>Python Pandas merge two data frames and append new data frame as a new column<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-genesis-blocks-gb-accordion gb-block-accordion\"><details><summary class=\"gb-accordion-title\">Show Solution<\/summary><div class=\"gb-accordion-text\"><div class=\"hljstoolbar\"><pre id=\"code11\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-13\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nCar_Price = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'Price'<\/span>: &#91;<span class=\"hljs-number\">23845<\/span>, <span class=\"hljs-number\">17995<\/span>, <span class=\"hljs-number\">135925<\/span> , <span class=\"hljs-number\">71400<\/span>]}\ncarPriceDf = pd.DataFrame.from_dict(Car_Price)\n\ncar_Horsepower = {<span class=\"hljs-string\">'Company'<\/span>: &#91;<span class=\"hljs-string\">'Toyota'<\/span>, <span class=\"hljs-string\">'Honda'<\/span>, <span class=\"hljs-string\">'BMV'<\/span>, <span class=\"hljs-string\">'Audi'<\/span>], <span class=\"hljs-string\">'horsepower'<\/span>: &#91;<span class=\"hljs-number\">141<\/span>, <span class=\"hljs-number\">80<\/span>, <span class=\"hljs-number\">182<\/span> , <span class=\"hljs-number\">160<\/span>]}\ncarsHorsepowerDf = pd.DataFrame.from_dict(car_Horsepower)\n\ncarsDf = pd.merge(carPriceDf, carsHorsepowerDf, on=<span class=\"hljs-string\">\"Company\"<\/span>)\ncarsDf<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-13\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code11', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div><\/div><\/details><\/div>\n","protected":false},"excerpt":{"rendered":"<p>This Pandas exercise project will help Python developers to learn and practice pandas. Pandas is an open-source, BSD-licensed Python library. Pandas is a handy and useful data-structure tool for analyzing large and complex data. Practice DataFrame, Data Selection, Group-By, Series, Sorting, Searching, statistics. Practice Data analysis using Pandas. In this exercise, we are using Automobile [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[21,22,8],"tags":[30],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.6 (Yoast SEO v22.6) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python Pandas Exercise<\/title>\n<meta name=\"description\" content=\"This Pandas exercise project is to help Python developer to learn and practice pandas by solving the questions and problems from the real world. When you complete each question you get more familiar with data analysis using pandas.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/pynative.com\/python-pandas-exercise\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Pandas Exercise\" \/>\n<meta property=\"og:description\" content=\"This Pandas exercise project is to help Python developer to learn and practice pandas by solving the questions and problems from the real world. When you complete each question you get more familiar with data analysis using pandas.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/pynative.com\/python-pandas-exercise\/\" \/>\n<meta property=\"og:site_name\" content=\"PYnative\" \/>\n<meta property=\"article:published_time\" content=\"2019-01-17T15:21:52+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-03-09T12:42:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise.png\" \/>\n<meta name=\"author\" content=\"Vishal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@PyNative\" \/>\n<meta name=\"twitter:site\" content=\"@PyNative\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Vishal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/\"},\"author\":{\"name\":\"Vishal\",\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f\"},\"headline\":\"Python Pandas Exercise\",\"datePublished\":\"2019-01-17T15:21:52+00:00\",\"dateModified\":\"2021-03-09T12:42:22+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/\"},\"wordCount\":391,\"commentCount\":50,\"publisher\":{\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f\"},\"image\":{\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise.png\",\"keywords\":[\"Python Exercises\"],\"articleSection\":[\"Pandas\",\"Python\",\"Python Exercises\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/pynative.com\/python-pandas-exercise\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/\",\"url\":\"https:\/\/pynative.com\/python-pandas-exercise\/\",\"name\":\"Python Pandas Exercise\",\"isPartOf\":{\"@id\":\"https:\/\/pynative.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/pynative.com\/python-pandas-exercise\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/python_pandas_exercise.png\",\"datePublished\":\"2019-01-17T15:21:52+00:00\",\"dateModified\":\"2021-03-09T12:42:22+00:00\",\"description\":\"This Pandas exercise project is to help Python developer to learn and practice pandas by solving the questions and problems from the real world. 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