{"id":76606,"date":"2025-02-06T21:01:40","date_gmt":"2025-02-06T21:01:40","guid":{"rendered":"http:\/\/bangla.sitestree.com\/?p=76606"},"modified":"2025-02-06T21:03:55","modified_gmt":"2025-02-06T21:03:55","slug":"python-matplotlib-plot-data","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=76606","title":{"rendered":"Python: MatPlotLib: Plot Data"},"content":{"rendered":"\n
import pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ncolumns = [\"J_ID\", \"P_ID\", \"D_ID\", \"Year\", \"JOB_TITLE\", \"Department_Name\", \"Total Salary\", \"Commission\"]\ndf = pd.read_csv(\"datamart.csv\", usecols=columns)\n\n\n# Create a 3D plot\nfig = plt.figure(figsize=(20, 15))\nax = fig.add_subplot(111, projection='3d')\n\n# Scatter plot\nax.scatter(df['Year'], df['J_ID'], df['D_ID'], c=df['Total Salary'], cmap='viridis', marker='X')\n\n# Labels\nax.set_xlabel('Year')\nax.set_ylabel('Job Title (Categorized)')\nax.set_zlabel('Departments')\n\n# Title\nax.set_title('3D Plot of Total Salary by Year and Job Title and Department')\n\n\nfor i in range(df.shape[0]):\n    if i%7 == 0:\n        ax.text(df['Year'][i], df['J_ID'][1], df['D_ID'][i], '(' + str(df['Year'][i]) + ',' + str(df[\"JOB_TITLE\"][i]) + ',' + str(df[\"Department_Name\"][i]) + ',' + str(df['Total Salary'][i]) + \")\")\n\n# Show plot\nplt.show()\n\n\n<\/code><\/pre>\n\n\n\n
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Sample Data:<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-76606","post","type-post","status-publish","format-standard","hentry","category-root","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":76075,"url":"http:\/\/bangla.sitestree.com\/?p=76075","url_meta":{"origin":76606,"position":0},"title":"K-Means Clustering","author":"Sayed","date":"May 18, 2024","format":false,"excerpt":"Click on the images to see them clearly #!\/usr\/bin\/env python coding: utf-8 In[1]: k-means clustering from numpy import unique from numpy import where from sklearn.datasets import make_classification from sklearn.cluster import KMeans from matplotlib import pyplot import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as\u2026","rel":"","context":"In "\u09ac\u09cd\u09b2\u0997 \u0964 Blog"","block_context":{"text":"\u09ac\u09cd\u09b2\u0997 \u0964 Blog","link":"http:\/\/bangla.sitestree.com\/?cat=182"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2024\/05\/image-40.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2024\/05\/image-40.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2024\/05\/image-40.png?resize=525%2C300 1.5x"},"classes":[]},{"id":17044,"url":"http:\/\/bangla.sitestree.com\/?p=17044","url_meta":{"origin":76606,"position":1},"title":"Calculate Your Categorized Expenses for Tax Purpose","author":"Sayed","date":"May 9, 2020","format":false,"excerpt":"Calculate Your Categorized Expenses for Tax Purpose. 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For the context, read the notes from Write Tax and Financial Software Code examples in relation to writing Tax and Financial Softwaremedium.com At this point, you have spreadsheets for\u2026","rel":"","context":"In "\u09ac\u09cd\u09b2\u0997 \u0964 Blog"","block_context":{"text":"\u09ac\u09cd\u09b2\u0997 \u0964 Blog","link":"http:\/\/bangla.sitestree.com\/?cat=182"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":26390,"url":"http:\/\/bangla.sitestree.com\/?p=26390","url_meta":{"origin":76606,"position":2},"title":"Basic Data Handling Functions in Python using pandas library #Python #Python #Introduction to Python","author":"Author-Check- Article-or-Video","date":"April 23, 2021","format":false,"excerpt":"\u00a0 # import pandas library import pandas as pd # read the online file by the URL provides above, and assign it to variable \"df\" path=\"https:\/\/......\" df = pd.read_csv(path,header=None) print(\"Done\") # show the first 5 rows using dataframe.head() method df.head(5) # create headers list headers = [\"name\", \"price\", \"make\"] #show\u2026","rel":"","context":"In "FromSitesTree.com"","block_context":{"text":"FromSitesTree.com","link":"http:\/\/bangla.sitestree.com\/?cat=1917"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":14558,"url":"http:\/\/bangla.sitestree.com\/?p=14558","url_meta":{"origin":76606,"position":3},"title":"Implement Gradient Descend:","author":"Sayed","date":"January 5, 2019","format":false,"excerpt":"\" Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function usinggradient descent, one takes steps proportional to the negative of the gradient (or approximategradient) of the function at the current point. Gradient descent - Wikipedia https:\/\/en.wikipedia.org\/wiki\/Gradient_descent\u2026","rel":"","context":"In "Math and Statistics for Data Science, and Engineering"","block_context":{"text":"Math and Statistics for Data Science, and Engineering","link":"http:\/\/bangla.sitestree.com\/?cat=1908"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":24915,"url":"http:\/\/bangla.sitestree.com\/?p=24915","url_meta":{"origin":76606,"position":4},"title":"Implement Gradient Descend: #Root","author":"Author-Check- Article-or-Video","date":"April 13, 2021","format":false,"excerpt":"\" Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. 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