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Jupyter vs Excel for Data Analysis
Jupyter | Excel |
Using jupyter will be command-based. Will take some time getting used to it. | Ease of Use with the Graphical User Interface (GUI). Learning formulas is fairly easier. |
Jupyter requires python language for data analysis hence a steeper learning curve. | Negligible previous knowledge is required. |
Equipped to handle lots of data really quickly. With the bonus of ease of accessibility to databases like Postgres and Mysql where actual data is stored. | Excel can only handle so much data. Scalability becomes difficult and messy. More Data = Slower Results |
Summary: Python is harder to learn because you have to download many packages and set the correct development environment on your computer. However, it provides a big leg up when working with big data and creating repeatable, automatable analyses, and in-depth visualizations. | Summary: Excel is best when doing small and one-time analyses or creating basic visualizations quickly. It is easy to become an intermediate user relatively without too much experience dueo its GUI. |
How to install and configure jupyter to analyze the datamart
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Table 1.1 - File Names for each Module
Module Name | Script File Name (With Links) | Datamart CSV File Name |
PT | ptDatamart.csv | |
W&S | waterDatamart.csv sewerageDatamart.csv | |
PGR | pgrDatamart.csv | |
mCollect | mcollectDatamart.csv | |
TL | tlDatamart.csv tlrenewDatamart.csv | |
Fire Noc | FNDatamart.csv | |
OBPS (Bpa) | bpaDatamart.csv | |
FSM | fsmDatamart.csv |
Table 1.2 - Pod Names for each Module
Module Name | Pod Name | Description |
PT | playground-865db67c64-tfdrk | Punjab Prod Data in UAT Environment |
W&S | playground-584d866dcc-cr5zf | QA Data |
PGR | Local Data | Data Dump |
mCollect | playground-584d866dcc-cr5zf | QA Data |
TL | playground-584d866dcc-cr5zf | QA Data |
Fire Noc | playground-584d866dcc-cr5zf | QA Data |
OBPS (Bpa) | playground-584d866dcc-cr5zf | QA Data |
FSM | playground-584d866dcc-cr5zf | QA Data |