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dev weycloud
2022-01-22 17:14:24 +01:00
parent 14f7b483f9
commit d8364a93b5
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.vscode/settings.json .vscode/settings.json
Hausarbeit/__pycache__
Beispielcode*

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Hausarbeit/Beispielcode von mir.md
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```
# https://towardsdatascience.com/five-regression-python-modules-that-every-data-scientist-must-know-a4e03a886853
# based on: https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/
# pip3 install openpyxl
import os
import pandas as pd # To read data
import math as m
import numpy as np
import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt # To visualize
# location will help to open files in the same directory as the py-script
__location__ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__)))
df = pd.read_excel(os.path.join(__location__,'Daten_Umfrage_SPSS_20211113.xlsx'))
df = df.apply(pd.to_numeric, errors='coerce') # convert non-numeric values to NaN, e.g. Header "row 1" "CodeXYZ" -> "row 1" "NaN"
print("Dataframe (Zeilen, Spalten, ...) inkl. NaN:", df.shape)
print(df.head(10))
# Code SE01_01 SE01_02 SE02_01 SE02_02 SE03_01
# 0 NaN NaN NaN NaN NaN NaN
# 1 NaN 6.0 4.0 7.0 4.0 5.0
# ...
#df = df.dropna() # CAUTION: drops every row that even contains single NaN !
print(df.tail(10))
# Code SE01_01 SE01_02 SE02_01 SE02_02 SE03_01
# 155 NaN 4.0 4.0 3.0 5.0 1.0
# 156 NaN NaN NaN NaN NaN NaN
# (End of File)
#print(df["HO_Score_Bewerbung_Gewichtet"][105:110])
#for col in df.columns:
#print(col)
# Calculate Mean, gew, inv
mwHO01_Diff = df["HO01_Diff"][1:156] # Limit to Column and row Amount
mwHO01_Diff = mwHO01_Diff.mean(skipna=True) # Columns arithm. mean, skipna to ignore NaN rows
mwHO01_Diff = round(mwHO01_Diff, 2)
normHO01_Diff = m.sqrt((mwHO01_Diff / 6)**2) # Norm
invHO01_Diff = 1 - normHO01_Diff # invert
# usw
print("HO01_Diff Mittelwert:", mwHO01_Diff)
print("HO01_Diff Normiert:", normHO01_Diff)
print("HO01_Diff Invertiert:", invHO01_Diff)
# usw
# Choose Dataframe Columns and row Amount
dfColumnX = df["SS_Score"][1:156]
dfColumnY = df["HO_Score_Bewerbung_Gewichtet"][1:156]
# Convert Dataframe Columns to Array containing the X- and Y- Values
arrX = np.asarray(dfColumnX) # convert that dataframe column to numpy array
arrY = np.asarray(dfColumnY)
# Prepare Plot Image
plt.xlabel('SS_Score', color='black')
plt.ylabel('HO_Score_Bewerbung_Gewichtet', color='black')
plt.xlim([0,50]) # set x-Axis View Range,[from,to]
plt.scatter(arrX, arrY)
arrX, arrY = zip(*sorted(zip(arrX,arrY))) # sort 2 arrays in sync
# Convert again, as sorting seemed to break the numpy array data format
arrX = np.asarray(arrX) # before: "1 16.0" after: "[16. 18. 21. ...]"
arrY = np.asarray(arrY)
# Use least Square Linear Regression from SciPy Stats
regr_results = sp.stats.linregress(arrX, arrY)
steigung = regr_results.slope
yAchsAbschn = regr_results.intercept
arrYpredicted = steigung * arrX + yAchsAbschn # using y = m*x + n, calculate every single Y-Value fitting the regression Lines X-Values
print("y =", steigung, "* x +", yAchsAbschn)
# Plot Linear Regression Line
plt.plot(arrX, arrYpredicted, label='Lin Regr', color='red', linestyle='solid') # https://scriptverse.academy/tutorials/python-matplotlib-plot-straight-line.html
plt.show()
```

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