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