Day 1- session¶
Brief History about Data Science¶
Professor Neil briefly explained the importants od data science with many examples as follow.
Image("images/Examples.png")
Practices¶
How to log into the Jupyterlab¶
There are many ways to log into jupyternotebook, however here we can simply log in using the link shared via our email. Simply need to open the mail with link and click there, automatically we will into the jupyternotebook page.¶
Documentation in jupyternotebook¶
It was really confusing for me since i am very new to this platform, i completely felt like i am just an Alien on the earth, however with help of coolegues and Anith sir i got little insight about it.But exploring and watching the tutorial i was able to document the work. However, failed to publish the work which was bit worrrisome yet i tried my best. Click here to watch the video
Documenting as per my knowledge¶
** To document our work in jupyternotebook; first thing we need to know the work area and its features. Once we are famalier with it, its very convenient to document our work. I have learned the following features first and started documenting my work.**
Types of folders in working page are:¶
- class folder
- Working folder with my name
From class folder i can get all resources covered by professor whereas in my folder i can document my work in detail. After that all the work i have done in my folder need to publish it. It was one of confusing session for me. It was really confusing so i messes not publishing any work done, though i did everything i lost the track to publish it.
Create weekly folder to work on assignment and hold our own work¶
i can create my own as follow but need to publish my work in my website
Need to link my work as follow:¶
-I have work inside the folder i have created for week 1 -need to changed, tracked, command and push the work -click on home.ipynb folder in working folder -will see week 1 cell -double click here -link the week1 folder there to diplay my work in my website.
usage of git¶
- Save Versions of Your Work
Git helps you save different versions of your code or documents so you can go back anytime if something goes wrong.
✅ 2. Track Changes
Git records who changed what and when, making your work organized and easy to follow.
✅ 3. Work Together Easily
Git allows many people to work on the same project, combine updates, and avoid mixing up files
Working File Edition¶
- Edit Code in a Cell
Click inside a code cell and type or change your code.
Press Shift + Enter to run it again.
- Edit Text in Markdown Cells
Click inside a Markdown cell to change text, headings, or links.
Press Shift + Enter to see the formatted result.
- Add or Delete Cells
Insert a new cell: Click Insert → Insert Cell Above/Below.
Delete a cell: Select the cell and click Delete (or ❌ button in toolbar).
- Move Cells Up or Down
Rearrange your work by selecting a cell and using the up ↑ or down ↓ buttons in the toolbar.
How to change and save the files or work in git¶
- got to git icon on left hand side
- There are two features: Changes & History
- got to changes feature
- under it, go to untracked & Changed accordingly
- click on {+} button
- -write over changes on summery dashboard below
- click on commit
- sucessful comit comment will apear
- then go to cloud like structure on above task bar
- will see orange dot
- click on it then your data or work will push and will be in respository folder
Types of Language in Jupyternotebook¶
-The jupyternotebook has its own lnguage. - There are 3 types of languahes -They are - code -raw -markdown and its very important know about it. since we need it to execute any work on jupyternotebook. usage of markdown is quite easy and found workable but code is very difficult for me to understand. And i am in fear thinking that how can i visualize the data in later part.
Cell Type¶
-Code: Write & run code
-Markdown: Writetext, formatted text, notes, headings etc..
-Raw: Keep text exactly as-is
How to run the cell¶
-click on run icon on taskbar [it is in triangular shape]
-just click Shift+Enter
Add the cell¶
-click on plus button on top
-click on box plus button on right side of recent cell
-press shift & enter
- can delete using delete icon on right hand side on existing cell
How to add hyperlink to the vedio or tutorial¶
Replace VIDEO_ID with the actual video ID or paste the full URL.
Press Shift + Enter to render.
Example:
markdown
Adding image¶
-upload the image using upload icon on taskbar
-display image inside the image folder inside our name folde eg dorjitshezom
-write the code
- <
- img 3.alt
- =
- ""
- src
- =
- "
- images/working page.png ** just pasteimage folder name-click copy the path**
- "
- width
- =
- "
- 600
- " 16.>
- we can add style and there are lot more option i have just kept it simple since i am really poor in code
- run it with markdown
Assignment on datasets¶
what is datasets?¶
A dataset is a collection of data organized in a structured way for analysis or processing.
First of all i explore the source from where i can download the datasets easily.¶
-And i came to laern there are many sources but from the session and through youtube i got to learned these five reliable data source:
Kaggle – https://www.kaggle.com/datasets
Huge collection of datasets for machine learning, data science, and competitions.
UCI Machine Learning Repository – https://archive.ics.uci.edu/ml/index.php
Classic datasets for research and learning.
Google Dataset Search – https://datasetsearch.research.google.com/
Search engine to find datasets across the web.
data.gov – https://www.data.gov/
Official U.S. government datasets, covering many topics.
AWS Open Data – https://registry.opendata.aws/
Large-scale datasets hosted by Amazon Web Services
I really wanted to know the sources of dataset in Bhutan and from CHATGPT i get to know following sources. And using it i also downlaoded the dataset from there and its is below.¶
How to download my datasets¶
-just went to data.gov.bt
-select the data
-dowloaded it
-with upload icon uploaded the datasets inside dataset folder
-used following syntax to run it or display my datasets on my jupyternotebook
-
What i did first before picking the datasets here in my notebook¶
1. import pandas in jupyter notebook environment:¶
-import pandas as follow
import pandas as pd
datasets = pd.read_excel ("Final_report_tables_2021AS.xlsx")
datasets
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | ... | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | Unnamed: 19 | Unnamed: 20 | Unnamed: 21 | Unnamed: 22 | Unnamed: 23 | Unnamed: 24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Irrigated Paddy | NaN | NaN | Upland Paddy | NaN | NaN | Maize | NaN | NaN | ... | NaN | Barley | NaN | NaN | Millet | NaN | NaN | Quinoa | NaN | NaN |
| 1 | Dzongkhag | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | ... | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) |
| 2 | Bumthang | 112.732886 | 108.73793 | 164.98479 | 0 | 0 | 0 | 0.95405 | 0.477025 | 0.276172 | ... | 303.913949 | 322.13337 | 284.32811 | 148.17781 | 2.950391 | 1.229329 | 0.983464 | 0.318016 | 0.318016 | 0.19081 |
| 3 | Chukha | 1047.123288 | 907.49028 | 1539.711124 | 55.618929 | 45.462151 | 30.29093 | 1494.45484 | 1153.47943 | 1446.529806 | ... | 121.949055 | 47.090664 | 41.795427 | 20.235387 | 362.342557 | 323.000712 | 147.827759 | 4.241636 | 3.50086 | 1.423413 |
| 4 | Dagana | 2067.202705 | 1862.639608 | 2450.662581 | 30.711222 | 29.497675 | 7.568256 | 2364.611317 | 1717.412537 | 2001.271484 | ... | 86.097623 | 50.407894 | 44.798978 | 19.547031 | 181.607689 | 163.633424 | 79.192313 | 0.020625 | 0.020625 | 0.010313 |
| 5 | Gasa | 96.28023 | 95.85609 | 129.405731 | 0 | 0 | 0 | 0.101794 | 0.101794 | 0.102507 | ... | 0 | 88.403578 | 88.403578 | 78.296633 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | Haa | 79.733842 | 72.28404 | 87.158778 | 11.206142 | 6.8386 | 2.632024 | 123.450042 | 92.860329 | 111.965722 | ... | 117.073964 | 54.164905 | 45.102258 | 21.944014 | 39.273974 | 33.101017 | 17.395902 | 0.493156 | 0.197262 | 0.098631 |
| 7 | Lhuentse | 775.698856 | 732.160847 | 1344.167739 | 42.796714 | 40.82313 | 37.667299 | 837.10642 | 742.14545 | 1241.239615 | ... | 0.276344 | 0.876041 | 0.265116 | 0.110465 | 64.07706 | 57.423392 | 37.073985 | 7.354234 | 6.173504 | 2.632887 |
| 8 | Mongar | 510.534181 | 474.609101 | 554.94201 | 32.450064 | 25.228099 | 19.3967 | 3757.768264 | 3179.649487 | 4369.035019 | ... | 37.59116 | 382.324291 | 339.815665 | 176.609771 | 18.64919 | 16.287487 | 7.470507 | 10.04975 | 10.046235 | 4.145776 |
| 9 | Paro | 2064.619382 | 1943.878563 | 5174.280382 | 2.280268 | 2.280268 | 0 | 23.419974 | 18.906335 | 9.847969 | ... | 33.651963 | 114.616724 | 105.655364 | 55.681227 | 3.391873 | 2.924622 | 2.080242 | 0 | 0 | 0 |
| 10 | Pema Gatshel | 13.870687 | 8.303635 | 7.839475 | 7.356314 | 6.430881 | 1.425101 | 1806.791599 | 1550.323932 | 4204.505938 | ... | 47.365862 | 10.619656 | 9.554694 | 5.171116 | 96.85006 | 89.922234 | 48.875159 | 0.613478 | 0.613478 | 0 |
| 11 | Punakha | 3331.304643 | 3084.985437 | 6509.573596 | 9.549422 | 6.982755 | 8.213701 | 159.099818 | 117.028312 | 147.753276 | ... | 28.570123 | 30.394357 | 28.523539 | 14.459545 | 1.401682 | 1.401682 | 0.729066 | 2.672176 | 2.672176 | 1.202479 |
| 12 | Samdrup Jongkhar | 1395.702944 | 1333.394074 | 2143.258362 | 26.978225 | 26.308587 | 4.997038 | 2138.034412 | 1990.913688 | 2750.260888 | ... | 334.525306 | 53.968285 | 52.358999 | 29.430664 | 78.440588 | 72.249176 | 34.631051 | 2.798599 | 2.798599 | 1.250664 |
| 13 | Samtse | 3568.334586 | 3261.784346 | 4011.487764 | 67.874905 | 56.215996 | 54.905197 | 3420.394966 | 2705.036622 | 2951.338215 | ... | 94.483006 | 11.744996 | 9.83607 | 3.599941 | 637.431767 | 570.47189 | 273.184071 | 12.879199 | 12.879199 | 4.322315 |
| 14 | Sarpang | 2986.751896 | 2672.574141 | 4202.028646 | 55.273175 | 52.259947 | 16.500786 | 2082.89788 | 1538.598138 | 2265.057124 | ... | 51.114088 | 0.652066 | 0.652066 | 0.173884 | 433.458823 | 401.192128 | 209.730817 | 3.154177 | 3.00658 | 1.051913 |
| 15 | Thimphu | 271.891408 | 233.83054 | 566.02558 | 3.113676 | 3.113676 | 0 | 42.04696 | 29.001387 | 7.944906 | ... | 4.393637 | 56.560976 | 43.13939 | 21.694171 | 1.03125 | 1.03125 | 0.61875 | 0 | 0 | 0 |
| 16 | Trashigang | 928.779432 | 822.66838 | 1505.769389 | 96.943459 | 76.607449 | 76.415062 | 2132.88839 | 1665.8931 | 3493.193651 | ... | 111.36855 | 84.675878 | 66.345809 | 36.905755 | 38.042749 | 34.757382 | 17.998744 | 31.968631 | 29.179726 | 14.894954 |
| 17 | Trashi Yangtse | 608.720176 | 538.577089 | 1014.921947 | 74.29526 | 62.987464 | 69.611825 | 758.485 | 622.32144 | 1520.870188 | ... | 4.089813 | 6.244887 | 5.965558 | 3.367283 | 189.462948 | 184.554054 | 104.320124 | 2.219538 | 2.019649 | 0.693819 |
| 18 | Trongsa | 1184.652712 | 989.07941 | 1573.956888 | 19.56991 | 13.912578 | 14.321315 | 421.07321 | 267.77272 | 467.312492 | ... | 147.81919 | 318.37923 | 281.47903 | 145.23953 | 43.195698 | 36.199705 | 17.234522 | 3.883309 | 2.265264 | 1.035549 |
| 19 | Tsirang | 1600.505245 | 1450.540697 | 1855.405991 | 45.593114 | 44.626006 | 0.629775 | 2097.007121 | 1516.746231 | 1923.444617 | ... | 37.951542 | 4.380883 | 3.252851 | 1.005365 | 169.944128 | 154.782719 | 65.150071 | 7.219005 | 5.910754 | 0.622459 |
| 20 | Wangdue Phodrang | 2593.454821 | 2244.607677 | 4470.44359 | 29.115146 | 22.053403 | 31.398577 | 143.925444 | 109.018382 | 195.176242 | ... | 192.832539 | 148.64483 | 140.629297 | 70.224927 | 19.378587 | 16.91441 | 7.728932 | 2.235162 | 1.814437 | 0.958545 |
| 21 | Zhemgang | 746.973923 | 625.409912 | 774.797194 | 102.980308 | 70.053027 | 51.103372 | 1668.90101 | 1271.65639 | 1831.515131 | ... | 100.250689 | 17.653857 | 12.938589 | 5.338336 | 132.826902 | 111.958915 | 49.880963 | 9.143486 | 5.144942 | 2.52767 |
| 22 | Bhutan | 25984.867844 | 23463.411797 | 40080.821555 | 713.706252 | 591.681692 | 427.076958 | 25473.412511 | 20289.342729 | 30938.640961 | ... | 1855.318404 | 1803.937369 | 1604.840388 | 857.212856 | 2513.757916 | 2273.035528 | 1122.106441 | 101.264178 | 88.561306 | 37.062198 |
23 rows × 25 columns
datasets = pd.read_excel("Final_report_tables_2021AS.xlsx")
datasets
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | ... | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | Unnamed: 19 | Unnamed: 20 | Unnamed: 21 | Unnamed: 22 | Unnamed: 23 | Unnamed: 24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Irrigated Paddy | NaN | NaN | Upland Paddy | NaN | NaN | Maize | NaN | NaN | ... | NaN | Barley | NaN | NaN | Millet | NaN | NaN | Quinoa | NaN | NaN |
| 1 | Dzongkhag | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | ... | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) | Sown Area (Acre) | Harvested Area (Acre) | Production (MT) |
| 2 | Bumthang | 112.732886 | 108.73793 | 164.98479 | 0 | 0 | 0 | 0.95405 | 0.477025 | 0.276172 | ... | 303.913949 | 322.13337 | 284.32811 | 148.17781 | 2.950391 | 1.229329 | 0.983464 | 0.318016 | 0.318016 | 0.19081 |
| 3 | Chukha | 1047.123288 | 907.49028 | 1539.711124 | 55.618929 | 45.462151 | 30.29093 | 1494.45484 | 1153.47943 | 1446.529806 | ... | 121.949055 | 47.090664 | 41.795427 | 20.235387 | 362.342557 | 323.000712 | 147.827759 | 4.241636 | 3.50086 | 1.423413 |
| 4 | Dagana | 2067.202705 | 1862.639608 | 2450.662581 | 30.711222 | 29.497675 | 7.568256 | 2364.611317 | 1717.412537 | 2001.271484 | ... | 86.097623 | 50.407894 | 44.798978 | 19.547031 | 181.607689 | 163.633424 | 79.192313 | 0.020625 | 0.020625 | 0.010313 |
| 5 | Gasa | 96.28023 | 95.85609 | 129.405731 | 0 | 0 | 0 | 0.101794 | 0.101794 | 0.102507 | ... | 0 | 88.403578 | 88.403578 | 78.296633 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | Haa | 79.733842 | 72.28404 | 87.158778 | 11.206142 | 6.8386 | 2.632024 | 123.450042 | 92.860329 | 111.965722 | ... | 117.073964 | 54.164905 | 45.102258 | 21.944014 | 39.273974 | 33.101017 | 17.395902 | 0.493156 | 0.197262 | 0.098631 |
| 7 | Lhuentse | 775.698856 | 732.160847 | 1344.167739 | 42.796714 | 40.82313 | 37.667299 | 837.10642 | 742.14545 | 1241.239615 | ... | 0.276344 | 0.876041 | 0.265116 | 0.110465 | 64.07706 | 57.423392 | 37.073985 | 7.354234 | 6.173504 | 2.632887 |
| 8 | Mongar | 510.534181 | 474.609101 | 554.94201 | 32.450064 | 25.228099 | 19.3967 | 3757.768264 | 3179.649487 | 4369.035019 | ... | 37.59116 | 382.324291 | 339.815665 | 176.609771 | 18.64919 | 16.287487 | 7.470507 | 10.04975 | 10.046235 | 4.145776 |
| 9 | Paro | 2064.619382 | 1943.878563 | 5174.280382 | 2.280268 | 2.280268 | 0 | 23.419974 | 18.906335 | 9.847969 | ... | 33.651963 | 114.616724 | 105.655364 | 55.681227 | 3.391873 | 2.924622 | 2.080242 | 0 | 0 | 0 |
| 10 | Pema Gatshel | 13.870687 | 8.303635 | 7.839475 | 7.356314 | 6.430881 | 1.425101 | 1806.791599 | 1550.323932 | 4204.505938 | ... | 47.365862 | 10.619656 | 9.554694 | 5.171116 | 96.85006 | 89.922234 | 48.875159 | 0.613478 | 0.613478 | 0 |
| 11 | Punakha | 3331.304643 | 3084.985437 | 6509.573596 | 9.549422 | 6.982755 | 8.213701 | 159.099818 | 117.028312 | 147.753276 | ... | 28.570123 | 30.394357 | 28.523539 | 14.459545 | 1.401682 | 1.401682 | 0.729066 | 2.672176 | 2.672176 | 1.202479 |
| 12 | Samdrup Jongkhar | 1395.702944 | 1333.394074 | 2143.258362 | 26.978225 | 26.308587 | 4.997038 | 2138.034412 | 1990.913688 | 2750.260888 | ... | 334.525306 | 53.968285 | 52.358999 | 29.430664 | 78.440588 | 72.249176 | 34.631051 | 2.798599 | 2.798599 | 1.250664 |
| 13 | Samtse | 3568.334586 | 3261.784346 | 4011.487764 | 67.874905 | 56.215996 | 54.905197 | 3420.394966 | 2705.036622 | 2951.338215 | ... | 94.483006 | 11.744996 | 9.83607 | 3.599941 | 637.431767 | 570.47189 | 273.184071 | 12.879199 | 12.879199 | 4.322315 |
| 14 | Sarpang | 2986.751896 | 2672.574141 | 4202.028646 | 55.273175 | 52.259947 | 16.500786 | 2082.89788 | 1538.598138 | 2265.057124 | ... | 51.114088 | 0.652066 | 0.652066 | 0.173884 | 433.458823 | 401.192128 | 209.730817 | 3.154177 | 3.00658 | 1.051913 |
| 15 | Thimphu | 271.891408 | 233.83054 | 566.02558 | 3.113676 | 3.113676 | 0 | 42.04696 | 29.001387 | 7.944906 | ... | 4.393637 | 56.560976 | 43.13939 | 21.694171 | 1.03125 | 1.03125 | 0.61875 | 0 | 0 | 0 |
| 16 | Trashigang | 928.779432 | 822.66838 | 1505.769389 | 96.943459 | 76.607449 | 76.415062 | 2132.88839 | 1665.8931 | 3493.193651 | ... | 111.36855 | 84.675878 | 66.345809 | 36.905755 | 38.042749 | 34.757382 | 17.998744 | 31.968631 | 29.179726 | 14.894954 |
| 17 | Trashi Yangtse | 608.720176 | 538.577089 | 1014.921947 | 74.29526 | 62.987464 | 69.611825 | 758.485 | 622.32144 | 1520.870188 | ... | 4.089813 | 6.244887 | 5.965558 | 3.367283 | 189.462948 | 184.554054 | 104.320124 | 2.219538 | 2.019649 | 0.693819 |
| 18 | Trongsa | 1184.652712 | 989.07941 | 1573.956888 | 19.56991 | 13.912578 | 14.321315 | 421.07321 | 267.77272 | 467.312492 | ... | 147.81919 | 318.37923 | 281.47903 | 145.23953 | 43.195698 | 36.199705 | 17.234522 | 3.883309 | 2.265264 | 1.035549 |
| 19 | Tsirang | 1600.505245 | 1450.540697 | 1855.405991 | 45.593114 | 44.626006 | 0.629775 | 2097.007121 | 1516.746231 | 1923.444617 | ... | 37.951542 | 4.380883 | 3.252851 | 1.005365 | 169.944128 | 154.782719 | 65.150071 | 7.219005 | 5.910754 | 0.622459 |
| 20 | Wangdue Phodrang | 2593.454821 | 2244.607677 | 4470.44359 | 29.115146 | 22.053403 | 31.398577 | 143.925444 | 109.018382 | 195.176242 | ... | 192.832539 | 148.64483 | 140.629297 | 70.224927 | 19.378587 | 16.91441 | 7.728932 | 2.235162 | 1.814437 | 0.958545 |
| 21 | Zhemgang | 746.973923 | 625.409912 | 774.797194 | 102.980308 | 70.053027 | 51.103372 | 1668.90101 | 1271.65639 | 1831.515131 | ... | 100.250689 | 17.653857 | 12.938589 | 5.338336 | 132.826902 | 111.958915 | 49.880963 | 9.143486 | 5.144942 | 2.52767 |
| 22 | Bhutan | 25984.867844 | 23463.411797 | 40080.821555 | 713.706252 | 591.681692 | 427.076958 | 25473.412511 | 20289.342729 | 30938.640961 | ... | 1855.318404 | 1803.937369 | 1604.840388 | 857.212856 | 2513.757916 | 2273.035528 | 1122.106441 | 101.264178 | 88.561306 | 37.062198 |
23 rows × 25 columns