import pandas as pd
information = {
"Title": ["Blade Runner", "2001: a space odyssey", "Alien"],
"12 months": [1982, 1968, 1979],
"MPA Score": ["R","G","R"]
}
df = pd.DataFrame(information)Purposes that use dataframes
As I beforehand talked about, most each information science library or framework helps a dataframe-like construction of some sort. The R language is mostly credited with popularizing the dataframe idea (though it existed in different varieties earlier than then). Spark, one of many first broadly well-liked platforms for processing information at scale, has its personal dataframe system. The Pandas information library for Python, and its speed-optimized cousin Polars, each provide dataframes. And the analytics database DuckDB combines the conveniences of dataframes with the facility of a full-blown database system.
It’s value noting the applying in query could help dataframe information codecs particular to that utility. As an illustration, Pandas gives information varieties for sparse information constructions in a dataframe. In contrast, Spark doesn’t have an specific sparse information sort, so any sparse-format information wants an extra conversion step for use in a Spark dataframe.
To that finish, whereas some libraries with dataframes are extra well-liked, there’s nobody definitive model of a dataframe. They’re a idea carried out by many various functions. Every implementation of a dataframe is free to do issues otherwise underneath the hood, and a few dataframe implementations differ within the end-user particulars, too.