mydatapreprocessing
Load data from web link or local file (json, csv, Excel file, parquet, h5...), consolidate it (resample data, clean NaN values, do string embedding) derive new features via columns derivation and do preprocessing like standardization or smoothing. If you want to see how functions works, check it's docstrings - working examples with printed results are also in tests - visual.py.
Links
Official readthedocs documentation
Installation
Python >=3.6 (Python 2 is not supported).
Install just with
pip install mydatapreprocessing
There are some libraries that not every user will be using (for some specific data inputs for example). If you want to be sure to have all libraries, you can provide extras requirements like.
pip install mydatapreprocessing[datatypes]
Available extras are ["all", "datasets", "datatypes"]
Examples
You can use live jupyter demo on binder
import mydatapreprocessing as mdp
import pandas as pd
import numpy as np
Load data
You can use:
- python formats (numpy.ndarray, pd.DataFrame, list, tuple, dict)
- local files
- web urls
Supported path formats are:
- csv
- xlsx and xls
- json
- parquet
- h5
You can load more data at once in list.
Syntax is always the same.
data = mdp.load_data.load_data(
"https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv",
)
# data2 = mdp.load_data.load_data([PATH_TO_FILE.csv, PATH_TO_FILE2.csv])
Consolidation
If you want to use data for some machine learning models, you will probably want to remove Nan values, convert string columns to numeric if possible, do encoding or keep only numeric data and resample.
Consolidation is working with pandas DataFrame as column names matters here.
There are many functions, but there is main function pipelining other functions consolidate_data
consolidation_config = mdp.consolidation.consolidation_config.default_consolidation_config.do.copy()
consolidation_config.datetime.datetime_column = 'Date'
consolidation_config.resample.resample = 'M'
consolidation_config.resample.resample_function = "mean"
consolidation_config.dtype = 'float32'
consolidated = mdp.consolidation.consolidate_data(data, consolidation_config)
print(consolidated.head())
Feature engineering
Functions in feature_engineering and preprocessing expects that data are in form (n_samples, n_features).
n_samples are usually much bigger and therefore transformed in consolidate_data if necessary.
In config, you can use shorter update dict syntax as all values names are unique.
Feature engineering
Create new columns that can be for example used as another machine learning model input.
import mydatapreprocessing.feature_engineering as mdpf
import mydatapreprocessing as mdp
data = pd.DataFrame(
[mdp.datasets.sin(n=30), mdp.datasets.ramp(n=30)]
).T
extended = mdpf.add_derived_columns(data, differences=True, rolling_means=10)
print(extended.columns)
print(f"\nit has less rows then on input {len(extended)}")
Functions in feature_engineering and preprocessing expects that data are in form (n_samples, n_features). n_samples are usually much bigger and therefore transformed in consolidate_data
if necessary.
Preprocessing
Preprocessing can be used on pandas DataFrame as well as on numpy array. Column names are not important as it's just matrix with defined dtype.
There is many functions, but there is main function pipelining other functions preprocess_data Preprocessed data can be converted back with preprocess_data_inverse
from mydatapreprocessing import preprocessing as mdpp
df = pd.DataFrame(np.array([range(5), range(20, 25), np.random.randn(5)]).astype("float32").T)
df.iloc[2, 0] = 500
config = mdpp.preprocessing_config.default_preprocessing_config.do.copy()
config.do.update({"remove_outliers": None, "difference_transform": True, "standardize": "standardize"})
data_preprocessed, inverse_config = mdpp.preprocess_data(df.values, config)
inverse_config.difference_transform = df.iloc[0, 0]
data_preprocessed_inverse = mdpp.preprocess_data_inverse(
data_preprocessed[:, 0], inverse_config
)
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Release files for mydatapreprocessing 3.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| mydatapreprocessing-3.0.3.tar.gz | 39.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mydatapreprocessing-3.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 91.9 kB
Release files / mydatapreprocessing-3.0.3.tar.gz
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