Not to taunt about the article, but the most important pandas parameters to me are `iterator=True` and `chunksize=x`, for streamed processing. Here's an example for processing a CSV file with 400 Million latitude and longitude coordinates.[1]<p>[1]: <a href="https://ad.vgiscience.org/twitter-global-preview/00_Twitter_datashader.html" rel="nofollow">https://ad.vgiscience.org/twitter-global-preview/00_Twitter_...</a>
I find pd.read_sql pretty useful for integration with SQLLite too.<p><a href="https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html#" rel="nofollow">https://pandas.pydata.org/docs/reference/api/pandas.read_sql...</a>
I’ve mostly replaced pd.read_csv and pd.read_parquet with duckdb.query(“select * from ‘x.csv’) or duckdb.query(“select * from ‘y/*.parquet’).<p>It’s much faster because DuckDB is vectorized. The result is a Pandas dataframe.<p>Querying the Pandas dataframe from DuckDB is faster than querying it with Pandas itself.
Some good tips in here, I've find myself reaching for JSON/excel methods often.<p>Despite using it for years, I still haven't decided if pandas is poorly architected or if the clunkiness (for lack of better of term) is a result of the inherent difficulty of the tasks.
Another tip: use <a href="https://github.com/sfu-db/connector-x" rel="nofollow">https://github.com/sfu-db/connector-x</a> to load database query result to pandas without memory copy resulting in faster operation.
I wish I knew about json_normalize sooner. JSON is great, but needing to transform it to a CSV/Excel sheet is a toil. Great to know about this one-liner!!
I spent some time working on something called DataProfiler python library<p><a href="https://github.com/capitalone/DataProfiler" rel="nofollow">https://github.com/capitalone/DataProfiler</a><p>The gist is that you can point to any common dataset and load it directly into pandas.<p>from dataprofiler import Data<p>data = Data("your_file.csv") # Auto-Detect & Load: CSV, AVRO, Parquet, JSON, Text, URL<p>I simply hate dealing with loading data, so it's my go-to.
My tip is to keep a dict of all the fields and the data types you expect them to be, particularly strings. In my company we have IDs that start with zeros, or are a mix of numbers and letters, and get interpreted as numeric types. I'm frequently pulling data out of the DW with the same fields, so I just have to use the dtype= arg point it to my dict and it takes care of that for me.
In my experience, the best way to load data into pandas is <a href="https://www.atlassian.com/software/bamboo" rel="nofollow">https://www.atlassian.com/software/bamboo</a>