On this page we’ll guide you through some of the most common operations used when doing data analysis. This is only a small subset of what’s possible in Polars. For more information, please visit the Documentation.
For the example we will use the Common Crawl statistics dataset. These statistics include: number of pages, distribution of top-level domains, crawl overlaps, etc. For more detailed information and graphs please visit their official statistics page.
import polars as pl
df = pl.read_csv(
"hf://datasets/commoncrawl/statistics/tlds.csv",
try_parse_dates=True,
)
df.head(3)
┌─────┬────────┬───────────────────┬────────────┬───┬───────┬──────┬───────┬─────────┐
│ ┆ suffix ┆ crawl ┆ date ┆ … ┆ pages ┆ urls ┆ hosts ┆ domains │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str ┆ date ┆ ┆ i64 ┆ i64 ┆ f64 ┆ f64 │
╞═════╪════════╪═══════════════════╪════════════╪═══╪═══════╪══════╪═══════╪═════════╡
│ 0 ┆ a.se ┆ CC-MAIN-2008-2009 ┆ 2009-01-12 ┆ … ┆ 18 ┆ 18 ┆ 1.0 ┆ 1.0 │
│ 1 ┆ a.se ┆ CC-MAIN-2009-2010 ┆ 2010-09-25 ┆ … ┆ 3462 ┆ 3259 ┆ 166.0 ┆ 151.0 │
│ 2 ┆ a.se ┆ CC-MAIN-2012 ┆ 2012-11-02 ┆ … ┆ 6957 ┆ 6794 ┆ 172.0 ┆ 150.0 │
└─────┴────────┴───────────────────┴────────────┴───┴───────┴──────┴───────┴─────────┘
The dataset contains some columns we don’t need. To remove them, we will use the select
method:
df = df.select("suffix", "date", "tld", "pages", "domains")
df.head(3)
┌────────┬───────────────────┬────────────┬─────┬───────┬─────────┐
│ suffix ┆ crawl ┆ date ┆ tld ┆ pages ┆ domains │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ date ┆ str ┆ i64 ┆ f64 │
╞════════╪═══════════════════╪════════════╪═════╪═══════╪═════════╡
│ a.se ┆ CC-MAIN-2008-2009 ┆ 2009-01-12 ┆ se ┆ 18 ┆ 1.0 │
│ a.se ┆ CC-MAIN-2009-2010 ┆ 2010-09-25 ┆ se ┆ 3462 ┆ 151.0 │
│ a.se ┆ CC-MAIN-2012 ┆ 2012-11-02 ┆ se ┆ 6957 ┆ 150.0 │
└────────┴───────────────────┴────────────┴─────┴───────┴─────────┘
We can filter the dataset using the filter
method. This method accepts complex expressions, but let’s start simple by filtering based on the crawl date:
import datetime
df = df.filter(pl.col("date") >= datetime.date(2020, 1, 1))
You can combine multiple predicates with &
or |
operators:
df = df.filter(
(pl.col("date") >= datetime.date(2020, 1, 1)) |
pl.col("crawl").str.contains("CC")
)
In order to add new columns to the dataset, use with_columns
. In the example below we calculate the total number of pages per domain and add a new column pages_per_domain
using the alias
method. The entire statement within with_columns
is called an expression. Read more about expressions and how to use them in the Polars user guide
df = df.with_columns(
(pl.col("pages") / pl.col("domains")).alias("pages_per_domain")
)
df.sample(3)
┌────────┬─────────────────┬────────────┬─────┬───────┬─────────┬──────────────────┐
│ suffix ┆ crawl ┆ date ┆ tld ┆ pages ┆ domains ┆ pages_per_domain │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ date ┆ str ┆ i64 ┆ f64 ┆ f64 │
╞════════╪═════════════════╪════════════╪═════╪═══════╪═════════╪══════════════════╡
│ net.bt ┆ CC-MAIN-2014-41 ┆ 2014-10-06 ┆ bt ┆ 4 ┆ 1.0 ┆ 4.0 │
│ org.mk ┆ CC-MAIN-2016-44 ┆ 2016-10-31 ┆ mk ┆ 1445 ┆ 430.0 ┆ 3.360465 │
│ com.lc ┆ CC-MAIN-2016-44 ┆ 2016-10-31 ┆ lc ┆ 1 ┆ 1.0 ┆ 1.0 │
└────────┴─────────────────┴────────────┴─────┴───────┴─────────┴──────────────────┘
In order to aggregate data together you can use the group_by
, agg
and sort
methods. Within the aggregation context you can combine expressions to create powerful statements which are still easy to read.
First, we aggregate all the data to the top-level domain tld
per scraped date:
df = df.group_by("tld", "date").agg(
pl.col("pages").sum(),
pl.col("domains").sum(),
)
Now we can calculate several statistics per top level domain:
df = df.group_by("tld").agg(
pl.col("date").unique().count().alias("number_of_scrapes"),
pl.col("domains").mean().alias("avg_number_of_domains"),
pl.col("pages").sort_by("date").pct_change().mean().alias("avg_page_growth_rate"),
)
df = df.sort("avg_number_of_domains", descending=True)
df.head(10)
┌─────┬───────────────────┬───────────────────────┬─────────────────────────────────┐
│ tld ┆ number_of_scrapes ┆ avg_number_of_domains ┆ avg_percent_change_in_number_o… │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ u32 ┆ f64 ┆ f64 │
╞═════╪═══════════════════╪═══════════════════════╪═════════════════════════════════╡
│ com ┆ 101 ┆ 1.9571e7 ┆ 0.022182 │
│ de ┆ 101 ┆ 1.8633e6 ┆ 0.5232 │
│ org ┆ 101 ┆ 1.5049e6 ┆ 0.019604 │
│ net ┆ 101 ┆ 1.5020e6 ┆ 0.021002 │
│ cn ┆ 101 ┆ 1.1101e6 ┆ 0.281726 │
│ ru ┆ 101 ┆ 1.0561e6 ┆ 0.416303 │
│ uk ┆ 101 ┆ 827453.732673 ┆ 0.065299 │
│ nl ┆ 101 ┆ 710492.623762 ┆ 1.040096 │
│ fr ┆ 101 ┆ 615471.594059 ┆ 0.419181 │
│ jp ┆ 101 ┆ 615391.455446 ┆ 0.246162 │
└─────┴───────────────────┴───────────────────────┴─────────────────────────────────┘