├── EDA(Sugarcane Production).ipynb ├── LICENSE ├── README.md └── List of Countries by Sugarcane Production.csv /EDA(Sugarcane Production).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 28, 6 | "id": "05b4e859", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "id": "42888b3e", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "df = pd.read_csv(\"List of \")" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3 (ipykernel)", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.11.1" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 5 45 | } 46 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Abbireddy Venkata Chandu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # Sugarcane Production Analysis 4 | 5 | This repository contains a Jupyter Notebook that performs an analysis of sugarcane production data using Python and popular data analysis libraries such as Pandas, Seaborn, and Matplotlib. The dataset used in this analysis is named "List of Countries by Sugarcane Production.csv." 6 | 7 | ### Setup 8 | Before running the code in the notebook, make sure you have the required libraries installed. If you don't have them, you can install them using pip: 9 | 10 | pip install pandas seaborn matplotlib 11 | 12 | ## Table of Contents 13 | 14 | - [Description](#description) 15 | - [Setup](#setup) 16 | - [Dataset](#dataset) 17 | - [Data Cleaning](#data-cleaning) 18 | - [Univariate Analysis](#univariate-analysis) 19 | - [Bivariate Analysis](#bivariate-analysis) 20 | - [Correlation Analysis](#correlation-analysis) 21 | - [Analysis by Continent](#analysis-by-continent) 22 | - [Conclusion](#conclusion) 23 | 24 | ## Dataset 25 | 26 | The dataset contains information about sugarcane production in various countries. It includes the following columns: 27 | 28 | - `Country`: The name of the country. 29 | - `Continent`: The continent where the country is located. 30 | - `Production(Tons)`: Total sugarcane production in tons. 31 | - `Production_per_person(Kg)`: Sugarcane production per person in kilograms. 32 | - `Acreage(Hectare)`: Total acreage of land used for sugarcane cultivation in hectares. 33 | - `Yield(Kg/Hectare)`: Yield of sugarcane in kilograms per hectare. 34 | 35 | ## Data Cleaning 36 | 37 | The initial data cleaning steps include removing unwanted characters (e.g., commas, dots) from numeric columns and dropping irrelevant columns. The future warnings during data cleaning are acknowledged but not significant to the analysis. 38 | 39 | ## Univariate Analysis 40 | 41 | The univariate analysis examines individual columns separately. It includes visualizations such as bar plots and distribution plots to understand the data distribution and identify outliers. 42 | 43 | ## Bivariate Analysis 44 | 45 | The bivariate analysis explores the relationships between two variables, such as land area vs. total production and yield per hectare vs. total production. Scatterplots and bar plots are used to visualize these relationships. 46 | 47 | ## Correlation Analysis 48 | 49 | The correlation analysis investigates the relationships between numerical variables. A heatmap is used to visualize the correlation matrix and identify any significant correlations. 50 | 51 | ## Analysis by Continent 52 | 53 | The analysis is also conducted at the continent level to understand how production and other variables vary across different continents. Bar plots and line plots are used to visualize these comparisons. 54 | 55 | ## Conclusion 56 | 57 | This analysis provides insights into sugarcane production across different countries and continents. It explores the relationship between various production-related metrics and uncovers patterns and trends within the dataset. 58 | 59 | For more details, please refer to the Jupyter Notebook in this repository. 60 | -------------------------------------------------------------------------------- /List of Countries by Sugarcane Production.csv: -------------------------------------------------------------------------------- 1 | ,Country,Continent,Production (Tons),Production per Person (Kg),Acreage (Hectare),Yield (Kg / Hectare) 2 | 0,Brazil,South America,768.678.382,"3.668,531",10.226.205,"75.167,5" 3 | 1,India,Asia,348.448.000,260721,4.950.000,"70.393,5" 4 | 2,China,Asia,123.059.739,88287,1.675.215,"73.459,1" 5 | 3,Thailand,Asia,87.468.496,"1.264,303",1.336.575,"65.442,2" 6 | 4,Pakistan,Asia,65.450.704,324219,1.130.820,57.879 7 | 5,Mexico,North America,56.446.821,452524,781.054,72.27 8 | 6,Colombia,South America,36.951.213,740075,416.626,"88.691,5" 9 | 7,Australia,Oceania,34.403.004,"1.373,406",447.204,"76.929,1" 10 | 8,Guatemala,North America,33.533.403,"1.938,114",259.85,"129.049,3" 11 | 9,United States of America,North America,29.926.210,91304,370.53,80.766 12 | 10,Indonesia,Asia,27.158.830,10248,472.693,"57.455,5" 13 | 11,Philippines,Asia,22.370.546,210232,410.104,"54.548,5" 14 | 12,Argentina,South America,21.990.823,494237,331.699,"66.297,5" 15 | 13,Cuba,North America,18.890.972,"1.683,528",442.307,"42.710,1" 16 | 14,Vietnam,Asia,16.313.145,172334,256.322,"63.643,2" 17 | 15,Egypt,Africa,15.760.418,161651,137.011,"115.030,6" 18 | 16,South Africa,Africa,15.074.610,261143,246.937,"61.046,3" 19 | 17,Myanmar,Asia,10.437.058,193771,163.65,"63.776,7" 20 | 18,Peru,South America,9.832.526,314768,87.696,"112.120,2" 21 | 19,Ecuador,South America,8.661.609,507518,104.661,"82.758,4" 22 | 20,Iran,Asia,7.687.593,9403,95.785,"80.259,1" 23 | 21,El Salvador,North America,7.202.141,"1.084,111",79.103,"91.047,6" 24 | 22,Kenya,Africa,7.094.619,139244,86.876,"81.663,7" 25 | 23,Bolivia,South America,6.910.805,61118,152.306,"45.374,5" 26 | 24,Nicaragua,North America,6.815.147,"1.084,393",74.13,"91.935,1" 27 | 25,Paraguay,South America,6.708.000,951087,120,55.9 28 | 26,Swaziland,Africa,5.583.295,"4.816,299",57.851,"96.511,4" 29 | 27,Sudan,Africa,5.525.059,135428,69.564,"79.424,5" 30 | 28,Honduras,North America,5.355.700,59427,64.666,"82.821,3" 31 | 29,Dominican Republic,North America,4.717.490,459519,106.81,44.167 32 | 30,Nepal,Asia,4.346.754,148765,80.931,"53.709,4" 33 | 31,Zambia,Africa,4.285.839,253784,41.695,"102.789,6" 34 | 32,Bangladesh,Asia,4.207.592,25481,98.357,"42.778,8" 35 | 33,Costa Rica,North America,4.158.370,83111,69.03,60.24 36 | 34,Mauritius,Africa,3.798.448,"3.002,994",51.477,"73.789,2" 37 | 35,Uganda,Africa,3.723.019,95897,54.911,"67.801,4" 38 | 36,Zimbabwe,Africa,3.483.000,234563,43.5,80.069 39 | 37,Venezuela,South America,3.331.252,104664,52.23,"63.780,4" 40 | 38,Madagascar,Africa,3.005.641,114445,94.157,"31.921,6" 41 | 39,Tanzania,Africa,2.994.127,55243,108.487,"27.599,1" 42 | 40,Malawi,Africa,2.915.406,162584,27.087,"107.629,5" 43 | 41,Mozambique,Africa,2.761.505,9568,42.311,"65.266,8" 44 | 42,Panama,North America,2.419.638,581814,37.995,"63.683,1" 45 | 43,Guyana,South America,2.394.553,"3.061,207",44.311,"54.039,7" 46 | 44,Congo-Kinshasa,Africa,2.191.333,2694,48.91,"44.803,4" 47 | 45,Laos,Asia,2.019.000,290036,36.18,"55.804,3" 48 | 46,Côte d'Ivoire,Africa,1.982.661,79606,25.205,"78.660,3" 49 | 47,Japan,Asia,1.574.000,12444,28.901,"54.461,6" 50 | 48,Fiji,Oceania,1.556.692,"1.759,199",36.705,"42.411,3" 51 | 49,Haiti,North America,1.472.712,132522,23.184,"63.521,9" 52 | 50,Belize,North America,1.457.656,"3.682,047",33.964,"42.917,7" 53 | 51,Jamaica,North America,1.422.432,521254,26.255,"54.178,2" 54 | 52,Ethiopia,Africa,1.410.312,13115,31.237,"45.148,8" 55 | 53,Nigeria,Africa,1.337.572,6776,82.586,"16.196,1" 56 | 54,Cameroon,Africa,1.288.732,54162,135.984,"9.477,1" 57 | 55,Trinidad and Tobago,North America,810,597066,15,54 58 | 56,Sri Lanka,Asia,747.907,34877,16.751,"44.647,8" 59 | 57,Senegal,Africa,696.992,44321,5.902,"118.085,3" 60 | 58,Congo-Brazzaville,Africa,687.365,127292,20.132,"34.142,4" 61 | 59,Cambodia,Asia,610.878,38014,27.387,"22.305,5" 62 | 60,Angola,Africa,556.094,19012,14.255,"39.011,6" 63 | 61,Burkina Faso,Africa,484.872,23951,4.823,"100.526,3" 64 | 62,Chad,Africa,455.986,297,4.433,"102.867,1" 65 | 63,Morocco,Africa,426.503,12265,10.434,"40.876,3" 66 | 64,Taiwan,Asia,395.8,16788,5.917,"66.896,6" 67 | 65,Uruguay,South America,367.7,104878,7.6,"48.381,6" 68 | 66,Mali,Africa,365.119,19108,5.035,"72.521,1" 69 | 67,Guinea,Africa,304.975,25664,5.683,"53.668,4" 70 | 68,Gabon,Africa,286.466,138553,4.645,"61.675,3" 71 | 69,Liberia,Africa,272.804,6225,26.781,"10.186,4" 72 | 70,Burundi,Africa,218.115,2042,2.998,"72.753,5" 73 | 71,Papua New Guinea,Oceania,217.866,25455,6.999,"31.128,9" 74 | 72,Niger,Africa,216.037,10064,5.84,"36.989,7" 75 | 73,Somalia,Africa,210.62,13873,5.731,"36.749,2" 76 | 74,Ghana,Africa,152.136,5137,6.122,"24.848,9" 77 | 75,Suriname,South America,125.286,220457,3.13,"40.022,4" 78 | 76,Central African Republic,Africa,103.002,21742,18.466,"5.577,8" 79 | 77,Saint Kitts and Nevis,North America,100,"2.164,315",1.8,"55.555,6" 80 | 78,Rwanda,Africa,93.823,7818,11.03,"8.506,3" 81 | 79,Barbados,North America,83.369,291105,1.733,"48.106,8" 82 | 80,Sierra Leone,Africa,77.269,10009,1.107,"69.811,3" 83 | 81,The Bahamas,North America,57.602,151059,2.308,"24.953,5" 84 | 82,Cape Verde,Africa,28.375,52152,1.296,"21.888,1" 85 | 83,Saint Vincent and the Grenadines,North America,17.871,163121,732,"24.428,9" 86 | 84,Afghanistan,Asia,17.364,55,1.333,"13.026,3" 87 | 85,Bhutan,Asia,14.6,20079,467,"31.252,7" 88 | 86,Benin,Africa,12.017,1058,598,"20.095,3" 89 | 87,Iraq,Asia,11.67,297,584,20 90 | 88,Saint Lucia,North America,10,55915,125,80 91 | 89,Grenada,North America,7.273,70388,162,"44.822,3" 92 | 90,Guinea-Bissau,Africa,6.864,4331,259,"26.498,8" 93 | 91,Malaysia,Asia,5.714,175,88,"65.223,8" 94 | 92,Portugal,Europe,5.429,528,62,"88.274,4" 95 | 93,Antigua and Barbuda,North America,5.365,6217,83,"64.638,6" 96 | 94,Dominica,North America,4.855,68099,244,"19.914,4" 97 | 95,Oman,Asia,1.186,237,51,"23.432,2" 98 | 96,Yemen,Asia,500,17,50,10 99 | 97,Spain,Europe,394,8,9,"43.596,5" 100 | 98,Lebanon,Asia,97,16,3,"28.386,4" 101 | 99,Djibouti,Africa,53,51,, 102 | 100,Singapore,Asia,50,9,2,25 103 | 101,Samoa,Oceania,12,6,1,"11.949,8" 104 | 102,Syria,Asia,1,0,0,"83.034,2" 105 | --------------------------------------------------------------------------------