🐼🤹♂️ pandas trick:
Two easy ways to reduce DataFrame memory usage:
1. Only read in columns you need
2. Use 'category' data type with categorical data
Example:
df = pd.read_csv('file.csv', usecols=['A', 'C', 'D'], dtype={'D':'category'})
#Python #DataScience
✴️ @AI_Python_EN
Two easy ways to reduce DataFrame memory usage:
1. Only read in columns you need
2. Use 'category' data type with categorical data
Example:
df = pd.read_csv('file.csv', usecols=['A', 'C', 'D'], dtype={'D':'category'})
#Python #DataScience
✴️ @AI_Python_EN
Quick links for all things #R and #Python:
1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ
For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG
✴️ @AI_Python_EN
1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ
For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG
✴️ @AI_Python_EN