A dead simple and easy to use Data Quality (DQ) tool built for Dataframes and Files with Python.
Tiny Timmy uses the Python bindings for Polars a Rust based DataFrame tool.
Support includes ...
polarspandaspysparkcsvfilesparquetfiles
Both dataframe and file support. Simply "point and shoot."
Install Tiny Timmy with pip
pip install tinytimmy
Create an instance of Tiny Timmy.
- specify
source_typepolarspandaspysparkcsvparquet
- specify either
file_pathordataframe
tm = TinyTim(source_type="csv", file_path="202306-divvy-tripdata.csv")
Then call either the default checks or a custom check.
results = tm.default_checks()
results = tm.run_custom_check(["{SQL filter}", "{SQL filter}"])
You can pass Tiny Timmy a dataframe while specifying what type it is (pandas, polars, pyspark)
and ask for default_checks, also you can simply pass a file uri to a csv or parquet file.
You can also pass custom DQ checks in the form of a list of SQL statements that would be found
in a nomral WHERE clause. Results of your checks are returned as a Polars dataframe.
The results of all Tiny Timmy checks are return as a Polars dataframe.
For example.
┌───────────────────────────────────┬─────────────┐
│ check_type ┆ check_value │
│ --- ┆ --- │
│ str ┆ i64 │
╞═══════════════════════════════════╪═════════════╡
│ null_check_start_station_name ┆ 978 │
│ null_check_start_station_id ┆ 978 │
│ … ┆ … │
│ started_at_whitespace_count ┆ 1000 │
│ ended_at_whitespace_count ┆ 1000 │
│ start_station_name_whitespace_co… ┆ 22 │
│ end_station_name_whitespace_coun… ┆ 22 │
Current functionality ...
default_checks()- check all columns for
nullvalues - check if dataset is distinct or contains duplicates
- check if columns have whitespace
- check for leading or trailing whitespace
- check all columns for
run_custom_check(["{some SQL WHERE clause}"])
CSV support.
tm = TinyTim(source_type="csv", file_path="202306-divvy-tripdata.csv")
results = tm.default_checks()
>> Column start_station_name has 978 null values
>> Column start_station_id has 978 null values
>> Column end_station_name has 978 null values
>> Column end_station_id has 978 null values
>> Your dataset has 45 duplicates
Pandas support.
df = pd.read_csv("202306-divvy-tripdata.csv")
tm = TinyTim(source_type="pandas", dataframe=df)
results = tm.default_checks()
>> Column start_station_name has 978 null values
>> Column start_station_id has 978 null values
>> Column end_station_name has 978 null values
>> Column end_station_id has 978 null values
>> Your dataset has no duplicates
Custom Data Quality checks are supported as a list of SQL based formats.
They are given as they would appear in a WHERE clause.
You can pass one or more checks in the list.
tm = TinyTim(source_type="csv", file_path="202306-divvy-tripdata.csv")
tm.default_checks()
results = tm.run_custom_check(["start_station_name IS NULL", "end_station_name IS NULL"])
Column start_station_name has 978 null values
Column start_station_id has 978 null values
Column end_station_name has 978 null values
Column end_station_id has 978 null values
Your dataset has no duplicates
Column started_at has 1000 whitespace values
Column ended_at has 1000 whitespace values
Column start_station_name has 22 whitespace values
Column end_station_name has 22 whitespace values
No leading or trailing whitespace values found
shape: (10, 2)
┌───────────────────────────────────┬─────────────┐
│ check_type ┆ check_value │
│ --- ┆ --- │
│ str ┆ i64 │
╞═══════════════════════════════════╪═════════════╡
│ null_check_start_station_name ┆ 978 │
│ null_check_start_station_id ┆ 978 │
│ null_check_end_station_name ┆ 978 │
│ null_check_end_station_id ┆ 978 │
│ … ┆ … │
└───────────────────────────────────┴─────────────┘
Your custom check start_station_name IS NULL found 978 records that match your filter statement
Your custom check end_station_name IS NULL found 978 records that match your filter statement
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