{"id":126394,"date":"2022-07-09T21:59:16","date_gmt":"2022-07-09T21:59:16","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=439075"},"modified":"2022-07-09T21:59:16","modified_gmt":"2022-07-09T21:59:16","slug":"how-to-get-the-last-n-rows-of-a-pandas-dataframe","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2022\/07\/09\/how-to-get-the-last-n-rows-of-a-pandas-dataframe\/","title":{"rendered":"How to Get the Last N Rows of a Pandas DataFrame?"},"content":{"rendered":"\n
\n
\n
\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n
\n
<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n
Rate this post<\/span> <\/div>\n<\/div>\n

In this tutorial we will unearth the solutions to three commonly asked questions that users come across while dealing with huge sets of data.<\/p>\n

Problem Formulation<\/strong><\/h2>\n

Given: <\/strong>Consider the following csv file (Note: You need to use it as a Pandas DataFrame).<\/p>\n

\"\"<\/figure>\n
import pandas as pd df = pd.read_csv('countries.csv')\nprint(df)<\/pre>\n
 Country Capital Population Area\n0 Germany Berlin 84,267,549 348,560\n1 France Paris 65,534,239 547,557\n2 Spain Madrid 46,787,468 498,800\n3 Italy Rome 60,301,346 294,140\n4 India Delhi 1,404,495,187 2,973,190\n5 USA Washington 334,506,463 9,147,420\n6 China Beijing 1,449,357,022 9,388,211\n7 Poland Warsaw 37,771,789 306,230\n8 Russia Moscow 146,047,418 16,376,870\n9 England London 68,529,747 241,930<\/pre>\n
Here’s the list of the questions that we will be focusing upon in this article:<\/p>\n