58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
# Getting and combining data
|
|
import pandas as pd
|
|
import numpy as np
|
|
import scout
|
|
from numpy import ndarray
|
|
from scipy.stats import binned_statistic
|
|
|
|
|
|
# Defining useful Functions to be used later
|
|
def slice_column(input_df, column, expression=" "):
|
|
unclean = input_df[column].to_list()
|
|
clean = list()
|
|
for record in unclean:
|
|
record = record.split(expression)[0]
|
|
clean.append(record)
|
|
|
|
input_df = input_df.drop(columns=[column])
|
|
input_df[column] = clean
|
|
return input_df
|
|
|
|
|
|
def write_joined_df(left, right, lsuf="new_key"):
|
|
merged = pd.concat([left, right], sort=False, ignore_index=True) # Good
|
|
return merged
|
|
|
|
|
|
def slam_dunk(dataset, column, labels):
|
|
min_value = dataset[column].min()
|
|
max_value = dataset[column].max()
|
|
print("min: ", min_value, " max: ", max_value)
|
|
bins = np.linspace(min_value, max_value, len(labels) + 1)
|
|
bins
|
|
|
|
dunked_column = "bin_" + column
|
|
dataset[dunked_column] = pd.cut(
|
|
dataset[column], bins=bins, labels=labels, include_lowest=True
|
|
)
|
|
|
|
dataset = scout.cure_depression(dataset)
|
|
|
|
x_data = np.arange(0, len(dataset))
|
|
y_data = dataset[column]
|
|
x_bins, bin_edges, misc = binned_statistic(
|
|
y_data, x_data, statistic="median", bins=len(labels)
|
|
)
|
|
bin_intervals = pd.IntervalIndex.from_arrays(bin_edges[:-1], bin_edges[1:])
|
|
dataset["bin_value"] = dataset[column].apply(
|
|
lambda x: set_to_median(x, bin_intervals)
|
|
)
|
|
|
|
return dataset
|
|
|
|
|
|
def set_to_median(x, bin_intervals):
|
|
for interval in bin_intervals:
|
|
if x in interval:
|
|
return interval.mid
|