kbeigan RenSys
RenSys
/ Pandas - groupby and transform
Last active
October 17, 2021 13:15
[Groupby & apply - Single Line] #pandas #groupby #apply
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| import pandas as pd | |
| df = pd.DataFrame([ | |
| [123, 'abc'], | |
| [123, 'abc'], | |
| [456, 'def'], | |
| [123, 'abc'], | |
| [123, 'abc'], | |
| [456, 'def'], | |
| [456, 'def'], |
RenSys
/ Pandas - Periodic Dataframes
Last active
May 3, 2019 06:34
Pandas - Dict of Periodic Dataframes
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| from glob import glob | |
| import pandas as pd | |
| folder = "data" | |
| filename_wildcard = f"{folder}/*.gz" | |
| tags = [ | |
| "timestamp", | |
| "01_dv_01_mi_daca_pv", | |
| "01_dv_01_si_daca_pv", |
RenSys
/ batch_matplotlib_diagram_dump.py
Last active
April 30, 2019 09:07
Matplotlib - pd.Series dump to matplotlib png
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| from glob import glob | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| sns.set_style("darkgrid") | |
| sns.set(rc = {"figure.figsize": (20, 10)}) | |
| sns.set(rc = {"figure.titlesize": "start-large"}) | |
| filename_wildcard = '*.gz' |
RenSys
/ TensorFlow - Basic Minimisation with Gradient Descent using placeholder data.ipynb
Created
May 11, 2018 07:31
TensorFlow - Basic Minimisation with Gradient Descent using placeholder data
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RenSys
/ TensorFlow - Basic Minimisation with Gradient Descent.ipynb
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May 11, 2018 07:19
TensorFlow - Basic Minimisation with Gradient Descent
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RenSys
/ Machine Learning - Generating Logscales for hyperparameter Arrays for tuning.ipynb
Created
May 8, 2018 21:41
Machine Learning - Generating Logscales for hyperparameter Arrays for tuning
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RenSys
/ Maths - Exponentially Moving Weighted Average.ipynb
Created
May 6, 2018 06:16
Maths - Exponentially Moving Weighted Average
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RenSys
/ Control Kalman Filter - Simple (youtube example).ipynb
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April 22, 2018 21:13
Control Kalman Filter - Simple (youtube example)
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RenSys
/ Numpy - Normal Distributed Random Values - np.random.randn and normal.ipynb
Last active
February 13, 2018 02:17
Numpy - Normal Distributed Random vectors and matrices using np.random.randn
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