Lab NumPy and SQL exercise

$$Try Converting SQL queries to pandas SQLDataScience$\_$Movies$\_01$

October $1,2024$

$```$

$[1]$: import pandas as pd

Movie $=$ pd$.$read$\_$csv$(\text{'}$movies$.$csv$\text{'})$

$```$

$[2]$: Movie $[\text{'}$ReleaseDate$\text{'}]=$ pd$.$to$\_$datetime$($Movie$[\text{'}$ReleaseDate$\text{'}],$ format$=\text{'}\backslash \%$d$-\backslash \%$b$-\backslash \%$y$\text{'})$ Movie

$[2]$:

$[7614$ rows x $15$ columns$]$

$```$

$[3]$: # SELECT Title

# FROM Movie

# WHERE Rating $="$G$"$

$[4]$: # SELECT Title

# FROM Movie

# WHERE ReleaseDate $>"2020-01-01"$

$```$

$[5]$: $\backslash$# SELECT Title

$\backslash$# FROM Movie

$\backslash$# WHERE Rating In $("$G$","$PG$")$

Movie.query$("$Rating$==\text{'}$G$\text{'}$ or Rating$==\text{'}$PG$\text{'}")[[\text{'}$Title$\text{'}]]$

$[5]$: Title

$0$ Superbabies: Baby Geniuses $2$

$3$ From Justin to Kelly

$5$ Leonard Part $6$

$6$ Son of the Mask

$15$ Dragonball Evolution

$-.$

$7574$

$7575$

Back to the Future

$7577$

Cinema Paradiso

The Lion King $7594$

$7598$ Star Wars: Episode V $-$ The Empire Strikes Back

$[1398$ rows x $1$ columns$]$

$[6]$: $\backslash$# SELECT Title

$\backslash$# FROM Movie

$\backslash$# WHERE Rating $="$PG$-13"$ and ReleaseDate $>"2008-01-01"$

$[7]$: $\backslash$# SELECT Title, ReleaseDate

$\backslash$# FROM Movie

$\backslash$# WHERE Budget $>$ Gross

$[8]$: $\backslash$# SELECT Title

$\backslash$# FROM Movie

$\backslash$# WHERE Budget $$ Gross AND Rating $="$G$"$

$[9]$: $\backslash$#SELECT Title, Year, Budget

$\backslash$# FROM Movie

$\backslash$# WHERE Budget $>300000000$