Admond Lee admond1994

Learning everyday. Python enthusiast, Machine learner, Deep learner

admond1994 / speech_recognition.py

Created June 22, 2019 06:11

	if __name__ == "__main__":
	recognizer = sr.Recognizer()
	mic = sr.Microphone(device_index=1)
	response = recognize_speech_from_mic(recognizer, mic)
	print('\nSuccess : {}\nError : {}\n\nText from Speech\n{}\n\n{}' \
	.format(response['success'],
	response['error'],
	'-'*17,
	response['transcription']))

admond1994 / recognize_speech_from_mic.py

Created June 22, 2019 05:55

	def recognize_speech_from_mic(recognizer, microphone):
	"""Transcribe speech from recorded from `microphone`.
	Returns a dictionary with three keys:
	"success": a boolean indicating whether or not the API request was
	successful
	"error": `None` if no error occured, otherwise a string containing
	an error message if the API could not be reached or
	speech was unrecognizable
	"transcription": `None` if speech could not be transcribed,
	otherwise a string containing the transcribed text

admond1994 / get_discrete_intervals_from_values.py

Created June 16, 2019 12:17

	def get_discrete_intervals_from_values(df):
	'''
	AIM -> get discrete intervals by binning values to a range

	INPUT -> df

	OUTPUT -> updated df with discrete intervals based on numerical values
	------
	'''
	df['rating'] = pd.cut(df['rating'],

admond1994 / get_unique_last_str.py

Created June 16, 2019 11:52

	def get_unique_last_str(df):
	'''
	AIM -> get unique last str for a column

	INPUT -> df

	OUTPUT -> updated df based on the unique last strings in a column
	------
	'''
	df = df[df.index.isin(df['col_1'].drop_duplicates(keep='last').index)].reset_index(drop=True)

admond1994 / remove_nan_values.py

Last active June 16, 2019 11:51

	def remove_nan_values(df):
	'''
	AIM -> remove NaN values of a particular column and output the whole dataframe

	INPUT -> df

	OUTPUT -> updated df without NaN values for a particular column
	------
	'''
	df = df[df['col_1'].notnull()]

admond1994 / list_comprehension.py

Created June 16, 2019 11:26

	def list_comprehension(df):
	'''
	AIM -> IF ELSE for the list comprehension

	INPUT -> df

	OUTPUT -> List
	------
	'''
	compute_list = [df['col_1'][i] if df['col_1'][i] > 0 else -1 for i in range(len(df))]

admond1994 / rename_col_name.py

Created June 16, 2019 09:54

	def rename_col(df):
	'''
	AIM -> rename column names

	INPUT -> df

	OUTPUT -> updated df with new column names
	------
	'''
	df.rename(index=str, columns={'col_1': 'new_col_1',

admond1994 / get_probability.py

Created May 17, 2019 14:17

	def get_probability(start_value, end_value, eval_points, kd):

	# Number of evaluation points
	N = eval_points
	step = (end_value - start_value) / (N - 1) # Step size

	x = np.linspace(start_value, end_value, N)[:, np.newaxis] # Generate values in the range
	kd_vals = np.exp(kd.score_samples(x)) # Get PDF values for each x
	probability = np.sum(kd_vals * step) # Approximate the integral of the PDF
	return probability.round(4)

admond1994 / plot_prob_density.py

Created May 17, 2019 14:16

	def plot_prob_density(df_lunch, df_dinner, field, x_start, x_end):
	plt.figure(figsize = (10, 7))

	unit = 1.5
	x = np.linspace(df_lunch.min() - unit, df_lunch.max() + unit, 1000)[:, np.newaxis]

	# Plot the data using a normalized histogram
	plt.hist(df_lunch, bins=10, density=True, label='Lunch Time', color='orange', alpha=0.2)
	plt.hist(df_dinner, bins=10, density=True, label='Dinner Time', color='navy', alpha=0.2)

admond1994 / bar_line_plot.py

Created January 25, 2019 15:35

	fig = plt.figure(figsize=(15, 7))
	ax1 = fig.add_subplot(111)

	ax1.set_xlabel('Month',fontsize=15)
	ax1.set_ylabel('Total Amount Spent ($)',fontsize=15)
	ax1.set_title('Total Amount Spent for different Months',fontsize=15)
	ax1.bar(df.groupby(by=['year_month'])['amount_spent'].sum().index.tolist()[1:],
	df.groupby(by=['year_month'])['amount_spent'].sum()[1:],
	alpha=0.85,
	label='Amount Spent by Month')