Zwischenstand
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115
Sonstiges/TENTW/auswertung.py
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115
Sonstiges/TENTW/auswertung.py
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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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###
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# Vorlesung 5
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###
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# ! Datensätze mit fehlenden Zellen für die HO-Spalten wurden zuvor schon in Excel rausgelöscht
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df = pd.read_excel(__location__ + '/Daten_Umfrage_SPSS_20211113.xlsx')
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print(df.head(10))
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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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df = df[1:156] # Limit to row amount
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# ! Vorher schon in Excel berechnet:
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# ! Die Spalten "HO_Score_Bewerbung_Roh" und "SS_Score"
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# ! Die Werte für die Differenz der Mittelwerte von HOx_1 und HOx_2 -> "MW", "Normiert", "Invertieren"
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# ! -> auf dieser Basis auch Die Spalte "HO_Score_Bewerbung_Gewichtet"
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# -> Berechnung für Diff "MW", "Normiert", "Invertieren" aber hier beispielhaft noch mal durchgeführt:
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mwHO01_Diff = df["HO01_Diff"] # Limit to Column
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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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###
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# Lineare Regression für x="SS_Score" und y="HO_Score_Bewerbung_Gewichtet"
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###
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# Choose Dataframe Columns and Row Amount
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dfColumnX = df["SS_Score"]
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dfColumnY = df["HO_Score_Bewerbung_Gewichtet"]
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# Convert Dataframe Columns to Numpy Array containing the X- and Y- Values
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arrX = np.asarray(dfColumnX) # before: "1 16.0" after: "[16. 18. 21. ...]"
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arrY = np.asarray(dfColumnY)
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arrX, arrY = zip(*sorted(zip(arrX,arrY))) # sort 2 arrays in sync
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# Convert again, as sorting restored the DataFrame-Format instead of numpy data array
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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 = round(regr_results.slope, 4)
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yAchsAbschn = round(regr_results.intercept, 4)
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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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# Show 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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plt.show()
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df["HO_Score_erklaert"] = yAchsAbschn + df["SS_Score"] * steigung
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# Vorlesung 6
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df["Diff_roh_erklaert"] = df["HO_Score_Bewerbung_Roh"] - df["HO_Score_erklaert"]
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df.loc[df.Diff_roh_erklaert < 0, "Diff_roh_erklaert"] = 0 # Differenz soll minimal 0 sein dürfen
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df["H0_Final"] = df["HO_Score_Bewerbung_Gewichtet"] + df["Diff_roh_erklaert"]
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df["H0_Wert_Z_Wert"] = (df["H0_Final"] - df["H0_Final"].mean()) / df["H0_Final"].std()
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df["H0_Wert_7er_Skala"] = (df["H0_Wert_Z_Wert"] * 1.5) + 4
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df["H0_Wert_7er_Skala"] = round(df["H0_Wert_7er_Skala"], 2)
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df.loc[df.H0_Wert_7er_Skala < 1, "H0_Wert_7er_Skala"] = 1 # ausreisser festlegen auf Min 1
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df.loc[df.H0_Wert_7er_Skala > 7, "H0_Wert_7er_Skala"] = 7 # ausreisser festelegen auf Max 7
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#df.to_csv(__location__ + '/tmpViewFile.csv', sep=";")
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# Open Dataframe in Webbrowser:
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def showDf(df):
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import webbrowser
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with open(__location__ + "/tmpViewFile.html", "w") as f:
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style = '<style> tr:nth-child(odd) { background-color: lightgrey; } </style>'
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dfHtml = df.to_html() + style
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f.write(dfHtml)
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webbrowser.open(__location__ + "/tmpViewFile.html")
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showDf(df)
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# https://towardsdatascience.com/five-regression-python-modules-that-every-data-scientist-must-know-a4e03a886853
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# or https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/
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# pip3 install openpyxl
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from numpy.matrixlib import defmatrix
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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].mean(skipna=True) # skipna to ignore NaN rows
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mwHO01_Diff = round(mwHO01_Diff, 2)
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gewHO01_Diff = m.sqrt((mwHO01_Diff / 6)**2)
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invHO01_Diff = 1 - gewHO01_Diff
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# usw
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print("HO01_Diff Mittelwert:", mwHO01_Diff)
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print("HO01_Diff Gewichtet:", gewHO01_Diff)
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print("HO01_Diff Invertiert:", invHO01_Diff)
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# usw
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# Limit Dataframe Column 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 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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25
Vorlesung 6/autos.py
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Vorlesung 6/autos.py
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import os
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import pandas as pd
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import openpyxl
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from scipy import stats
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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_csv(__location__ + '/autos.txt', sep=";")
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df = df[:12]
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print(df)
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mw = df.mean()
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print(mw)
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sta = df.std()
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print(sta)
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analysis = pd.DataFrame({"Mittelwert": mw, "Standardabw.": sta})
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print(analysis)
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analysis.to_excel(__location__ + "/auswertung.xlsx")
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corr = stats.pearsonr(df["Weight"], df["Volume"])
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print("Corr:", corr)
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37
Vorlesung 6/autos.txt
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37
Vorlesung 6/autos.txt
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Car;Model;Volume;Weight;CO2
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Toyota;Aygo;1000;790;99
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Mitsubishi;Space Star;1200;1160;95
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Skoda;Citigo;1000;929;95
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Fiat;500;900;865;90
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Mini;Cooper;1500;1140;105
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VW;Up!;1000;929;105
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Skoda;Fabia;1400;1109;90
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Mercedes;A-Class;1500;1365;92
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Ford;Fiesta;1500;1112;98
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Audi;A1;1600;1150;99
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Hyundai;I20;1100;980;99
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Suzuki;Swift;1300;990;101
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Ford;Fiesta;1000;1112;99
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Honda;Civic;1600;1252;94
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Hundai;I30;1600;1326;97
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Opel;Astra;1600;1330;97
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BMW;1;1600;1365;99
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Mazda;3;2200;1280;104
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Skoda;Rapid;1600;1119;104
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Ford;Focus;2000;1328;105
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Ford;Mondeo;1600;1584;94
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Opel;Insignia;2000;1428;99
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Mercedes;C-Class;2100;1365;99
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Skoda;Octavia;1600;1415;99
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Volvo;S60;2000;1415;99
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Mercedes;CLA;1500;1465;102
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Audi;A4;2000;1490;104
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Audi;A6;2000;1725;114
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Volvo;V70;1600;1523;109
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BMW;5;2000;1705;114
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Mercedes;E-Class;2100;1605;115
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Volvo;XC70;2000;1746;117
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Ford;B-Max;1600;1235;104
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BMW;2;1600;1390;108
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Opel;Zafira;1600;1405;109
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Mercedes;SLK;2500;1395;120
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