EO Dashboard Hackathon

wātha | Spatial Analysis and Time Series

	def fis_data_to_dataframe(fis_data):
	""" Creates a DataFrame from list of FIS responses
	"""
	COLUMNS = ['channel', 'date', 'min', 'max', 'mean', 'stDev']
	data = []

	for fis_response in fis_data:
	for channel, channel_stats in fis_response.items():
	for stat in channel_stats:
	row = [int(channel[1:]), parse_time(stat['date'], force_datetime=True)]

	for column in COLUMNS[2:]:
	row.append(stat['basicStats'][column])

	data.append(row)

	return pd.DataFrame(data, columns=COLUMNS).sort_values(['channel', 'date'])


	df = fis_data_to_dataframe(fis_data)

	df.head()

VANODHYA WARNASOORIYA

-def remove_outlier(df_in, col_name):
- q1 = df_in[col_name].quantile(0.25)
- q3 = df_in[col_name].quantile(0.75)
- iqr = q3-q1 #Interquartile range
- fence_low = q1-1.5*iqr
- fence_high = q3+1.5*iqr
- df_out = df_in.loc[(df_in[col_name] > fence_low) & (df_in[col_name] < fence_high)]
- return df_out

Suvin Nimnaka

VANODHYA WARNASOORIYA