{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c7ee241f-c0dd-4e15-b196-da2d12ce0601", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "id": "2184cef4-5ad2-41e2-912a-bdc9ceb08280", "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv(\"C:\\\\Users\\\\daroo\\\\Downloads\\\\Global YouTube Statistics.csv\",encoding=\"latin1\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "c0397479-55fb-45f5-aee3-958bdcc7f538", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Shape: (995, 28)\n" ] }, { "data": { "text/html": [ "
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rankYoutubersubscribersvideo viewscategoryTitleuploadsCountryAbbreviationchannel_type...subscribers_for_last_30_dayscreated_yearcreated_monthcreated_dateGross tertiary education enrollment (%)PopulationUnemployment rateUrban_populationLatitudeLongitude
01T-Series2450000002.280000e+11MusicT-Series20082IndiaINMusic...2000000.02006.0Mar13.028.11.366418e+095.36471031528.020.59368478.962880
12YouTube Movies1700000000.000000e+00Film & Animationyoutubemovies1United StatesUSGames...NaN2006.0Mar5.088.23.282395e+0814.70270663028.037.090240-95.712891
23MrBeast1660000002.836884e+10EntertainmentMrBeast741United StatesUSEntertainment...8000000.02012.0Feb20.088.23.282395e+0814.70270663028.037.090240-95.712891
34Cocomelon - Nursery Rhymes1620000001.640000e+11EducationCocomelon - Nursery Rhymes966United StatesUSEducation...1000000.02006.0Sep1.088.23.282395e+0814.70270663028.037.090240-95.712891
45SET India1590000001.480000e+11ShowsSET India116536IndiaINEntertainment...1000000.02006.0Sep20.028.11.366418e+095.36471031528.020.59368478.962880
\n", "

5 rows × 28 columns

\n", "
" ], "text/plain": [ " rank Youtuber subscribers video views \\\n", "0 1 T-Series 245000000 2.280000e+11 \n", "1 2 YouTube Movies 170000000 0.000000e+00 \n", "2 3 MrBeast 166000000 2.836884e+10 \n", "3 4 Cocomelon - Nursery Rhymes 162000000 1.640000e+11 \n", "4 5 SET India 159000000 1.480000e+11 \n", "\n", " category Title uploads Country \\\n", "0 Music T-Series 20082 India \n", "1 Film & Animation youtubemovies 1 United States \n", "2 Entertainment MrBeast 741 United States \n", "3 Education Cocomelon - Nursery Rhymes 966 United States \n", "4 Shows SET India 116536 India \n", "\n", " Abbreviation channel_type ... subscribers_for_last_30_days \\\n", "0 IN Music ... 2000000.0 \n", "1 US Games ... NaN \n", "2 US Entertainment ... 8000000.0 \n", "3 US Education ... 1000000.0 \n", "4 IN Entertainment ... 1000000.0 \n", "\n", " created_year created_month created_date \\\n", "0 2006.0 Mar 13.0 \n", "1 2006.0 Mar 5.0 \n", "2 2012.0 Feb 20.0 \n", "3 2006.0 Sep 1.0 \n", "4 2006.0 Sep 20.0 \n", "\n", " Gross tertiary education enrollment (%) Population Unemployment rate \\\n", "0 28.1 1.366418e+09 5.36 \n", "1 88.2 3.282395e+08 14.70 \n", "2 88.2 3.282395e+08 14.70 \n", "3 88.2 3.282395e+08 14.70 \n", "4 28.1 1.366418e+09 5.36 \n", "\n", " Urban_population Latitude Longitude \n", "0 471031528.0 20.593684 78.962880 \n", "1 270663028.0 37.090240 -95.712891 \n", "2 270663028.0 37.090240 -95.712891 \n", "3 270663028.0 37.090240 -95.712891 \n", "4 471031528.0 20.593684 78.962880 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Original Shape:\",df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "d6b055d3-a5e8-4490-8549-4a570e58ad3d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "category 46\n", "Country 122\n", "Abbreviation 122\n", "channel_type 30\n", "video_views_rank 1\n", "country_rank 116\n", "channel_type_rank 33\n", "video_views_for_the_last_30_days 56\n", "subscribers_for_last_30_days 337\n", "created_year 5\n", "created_month 5\n", "created_date 5\n", "Gross tertiary education enrollment (%) 123\n", "Population 123\n", "Unemployment rate 123\n", "Urban_population 123\n", "Latitude 123\n", "Longitude 123\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()[df.isnull().sum()>0]" ] }, { "cell_type": "code", "execution_count": 8, "id": "0e24fd40-17b9-4b81-bec0-7c471393daf6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "rank int64\n", "Youtuber object\n", "subscribers int64\n", "video views float64\n", "category object\n", "Title object\n", "uploads int64\n", "Country object\n", "Abbreviation object\n", "channel_type object\n", "video_views_rank float64\n", "country_rank float64\n", "channel_type_rank float64\n", "video_views_for_the_last_30_days float64\n", "lowest_monthly_earnings float64\n", "highest_monthly_earnings float64\n", "lowest_yearly_earnings float64\n", "highest_yearly_earnings float64\n", "subscribers_for_last_30_days float64\n", "created_year float64\n", "created_month object\n", "created_date float64\n", "Gross tertiary education enrollment (%) float64\n", "Population float64\n", "Unemployment rate float64\n", "Urban_population float64\n", "Latitude float64\n", "Longitude float64\n", "dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 9, "id": "c4a4d336-fb39-4d14-a23c-99812003dd06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape after removing 1970 rows: (994, 28)\n" ] } ], "source": [ "df=df[df[\"created_year\"]!=1970]\n", "print(\"Shape after removing 1970 rows:\",df.shape)" ] }, { "cell_type": "code", "execution_count": 10, "id": "e7f550c6-d8ed-4d54-957b-e8f3fef0ab35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape After removing zero-upload/zero-view rows: (948, 28)\n" ] } ], "source": [ "df=df[df[\"uploads\"]>0]\n", "df=df[df[\"video views\"]>0]\n", "print(\"Shape After removing zero-upload/zero-view rows:\",df.shape)" ] }, { "cell_type": "code", "execution_count": 11, "id": "54e0ddc9-b6b2-4d58-9901-09ed9bc248ae", "metadata": {}, "outputs": [], "source": [ "df[\"Country\"]=df[\"Country\"].fillna(\"Unknown\")\n", "df[\"Abbreviation\"]=df[\"Abbreviation\"].fillna(\"N/A\")\n", "df[\"channel_type\"]=df[\"channel_type\"].fillna(\"Unknown\")\n", "df[\"category\"]=df[\"category\"].fillna(\"Unknown\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "e097254b-ddb6-4f9a-b2aa-2e32119f4adc", "metadata": {}, "outputs": [], "source": [ "country_level_cols = [\n", " \"Gross tertiary education enrollment (%)\",\n", " \"Population\",\n", " \"Unemployment rate\",\n", " \"Urban_population\",\n", " \"Latitude\",\n", " \"Longitude\",\n", "]\n", "for col in country_level_cols:\n", " df[col]=df[col].fillna(df[col].median())" ] }, { "cell_type": "code", "execution_count": 32, "id": "b9f4e547-948c-4c60-ae3b-6704c24b9894", "metadata": {}, "outputs": [], "source": [ "rank_cols = [\"video_views_rank\", \"country_rank\", \"channel_type_rank\"]\n", "for col in rank_cols:\n", " df[col]=df[col].fillna(df[col].median())\n", "df[\"video_views_for_the_last_30_days\"] = df[\"video_views_for_the_last_30_days\"].fillna(0)\n", "df[\"subscribers_for_last_30_days\"] = df[\"subscribers_for_last_30_days\"].fillna(0)" ] }, { "cell_type": "code", "execution_count": 33, "id": "1a8150db-9417-4636-8316-6c7d6b5b1e7f", "metadata": {}, "outputs": [], "source": [ "df=df.dropna(subset=[\"created_year\",\"created_month\",\"created_date\"])" ] }, { "cell_type": "code", "execution_count": 34, "id": "e777fec4-2e8e-44b6-b97b-f05e37ae71ca", "metadata": {}, "outputs": [], "source": [ "df[\"created_year\"]=df[\"created_year\"].astype(int)\n", "df[\"created_date\"]=df[\"created_date\"].astype(int)" ] }, { "cell_type": "code", "execution_count": 35, "id": "75f1dd57-4b6b-499e-88e1-fe708134720c", "metadata": {}, "outputs": [], "source": [ "df=df.drop_duplicates()\n", "df=df.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 36, "id": "bdf7fd85-93f7-43e1-b34d-881dbf7c8381", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved.\n", " Unnamed: 0 rank Youtuber subscribers video views \\\n", "0 0 1 T-Series 245000000 2.280000e+11 \n", "1 2 3 MrBeast 166000000 2.836884e+10 \n", "2 3 4 Cocomelon - Nursery Rhymes 162000000 1.640000e+11 \n", "3 4 5 SET India 159000000 1.480000e+11 \n", "4 6 7 ýýý Kids Diana Show 112000000 9.324704e+10 \n", "\n", " category Title uploads Country \\\n", "0 Music T-Series 20082 India \n", "1 Entertainment MrBeast 741 United States \n", "2 Education Cocomelon - Nursery Rhymes 966 United States \n", "3 Shows SET India 116536 India \n", "4 People & Blogs ýýý Kids Diana Show 1111 United States \n", "\n", " Abbreviation ... subscribers_for_last_30_days created_year created_month \\\n", "0 IN ... 2000000.0 2006 Mar \n", "1 US ... 8000000.0 2012 Feb \n", "2 US ... 1000000.0 2006 Sep \n", "3 IN ... 1000000.0 2006 Sep \n", "4 US ... 0.0 2015 May \n", "\n", " created_date Gross tertiary education enrollment (%) Population \\\n", "0 13 28.1 1.366418e+09 \n", "1 20 88.2 3.282395e+08 \n", "2 1 88.2 3.282395e+08 \n", "3 20 28.1 1.366418e+09 \n", "4 12 88.2 3.282395e+08 \n", "\n", " Unemployment rate Urban_population Latitude Longitude \n", "0 5.36 471031528.0 20.593684 78.962880 \n", "1 14.70 270663028.0 37.090240 -95.712891 \n", "2 14.70 270663028.0 37.090240 -95.712891 \n", "3 5.36 471031528.0 20.593684 78.962880 \n", "4 14.70 270663028.0 37.090240 -95.712891 \n", "\n", "[5 rows x 29 columns]\n" ] } ], "source": [ "df.to_csv(r\"C:\\Users\\daroo\\Downloads\\Global_YouTube_Statistics_Cleaned.csv\", index=False)\n", "print(\"Saved.\")\n", "print(df.head())" ] }, { "cell_type": "code", "execution_count": null, "id": "2590ef93-799f-4486-9cee-0ccbc84989d1", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }