Colin Dietrich crdietrich

## Dog Classification Profiler.ipynb

      
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                crdietrich
                / Dog Classification Profiler.ipynb
            
            
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              May 28, 2019 23:49
            
          
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## cmatrix.py
"""Calculate a Confusion Matrix for multi-class classification
model results

2019 Colin Dietrich
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

## useful_pandas_snippets.py
# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]

## matplotlib_obvious.py
# -*- coding: utf-8 -*-
"""
Colors and markers for contrast plotting large numbers of series
Created on Fri Sep  2 10:31:04 2016
@author: Colin Dietrich
"""

import colorsys
from math import pi

## peakdetect.py
import numpy as np
from math import pi, log
import pylab
from scipy import fft, ifft
from scipy.optimize import curve_fit

i = 10000
x = np.linspace(0, 3.5 * pi, i)
y = (0.3*np.sin(x) + np.sin(1.3 * x) + 0.9 * np.sin(4.2 * x) + 0.06 *
    np.random.randn(i))
	"""Calculate a Confusion Matrix for multi-class classification
	model results

	2019 Colin Dietrich
	"""

	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	# List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	# Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	# Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(valuelist)]
	# -- coding: utf-8 --
	"""
	Colors and markers for contrast plotting large numbers of series
	Created on Fri Sep 2 10:31:04 2016
	@author: Colin Dietrich
	"""

	import colorsys
	from math import pi
	import numpy as np
	from math import pi, log
	import pylab
	from scipy import fft, ifft
	from scipy.optimize import curve_fit

	i = 10000
	x = np.linspace(0, 3.5 * pi, i)
	y = (0.3np.sin(x) + np.sin(1.3 x) + 0.9 * np.sin(4.2 * x) + 0.06 *
	np.random.randn(i))