kbeigan RenSys

## Pandas - groupby and transform
import pandas as pd

df = pd.DataFrame([
    [123, 'abc'],
    [123, 'abc'],
    [456, 'def'],
    [123, 'abc'],
    [123, 'abc'],
    [456, 'def'],
    [456, 'def'],

## Pandas - Periodic Dataframes
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",

## batch_matplotlib_diagram_dump.py
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'

## TensorFlow - Basic Minimisation with Gradient Descent using placeholder data.ipynb

      
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                / TensorFlow - Basic Minimisation with Gradient Descent using placeholder data.ipynb
            
            
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              May 11, 2018 07:31
            
              
                TensorFlow - Basic Minimisation with Gradient Descent using placeholder data
              
          
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## 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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## Numpy - Normalizing Data.ipynb

      
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                / Numpy - Normalizing Data.ipynb
            
            
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              May 10, 2018 23:58
            
              
                Numpy - Normalizing Data
              
          
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## Machine Learning - Generating Logscales for hyperparameter Arrays for tuning.ipynb

      
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                / Machine Learning - Generating Logscales for hyperparameter Arrays for tuning.ipynb
            
            
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              May 8, 2018 21:41
            
              
                Machine Learning - Generating Logscales for hyperparameter Arrays for tuning
              
          
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## Maths - Exponentially Moving Weighted Average.ipynb

      
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                / Maths - Exponentially Moving Weighted Average.ipynb
            
            
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              May 6, 2018 06:16
            
              
                Maths - Exponentially Moving Weighted Average
              
          
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## 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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## Numpy - Normal Distributed Random Values - np.random.randn and normal.ipynb

      
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                / Numpy - Normal Distributed Random Values - np.random.randn and normal.ipynb
            
            
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              February 13, 2018 02:17
            
              
                Numpy - Normal Distributed Random vectors and matrices using np.random.randn
              
          
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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'],
	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",
	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'