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78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
# Author - Kristiāns Francis Cagulis
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# Date - 04.02.2022
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# Title - Classwork
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import pandas as pd
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from word2number import w2n
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import seaborn as sns
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import matplotlib
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# matplotlib.use('Qt5Agg')
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import matplotlib.pyplot as plt
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# mathplotlib ir bibliotēka statisku, animētu un interaktīvu vizualizāciju izveidei
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# seaborn padara matplotlib sarežģītākos momentus par vienkāršākiem
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def get_data():
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data = pd.read_csv("auto_imports_mainits.csv")
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data_copy = data.copy()
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del data_copy["normalized-losses"]
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dislike = ["N/A", "NA", "--"]
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data_copy3 = pd.read_csv("auto_imports_mainits.csv", na_values=dislike)
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# Replaces word written numbers to intigers
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columns = ["num-of-doors", "num-of-cylinders"]
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for column in columns:
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for value in data_copy3[column]:
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try:
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data_copy3 = data_copy3.replace(
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to_replace=value, value=w2n.word_to_num(value))
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except:
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pass
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print(data_copy3[["num-of-doors", "num-of-cylinders"]])
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# Leaves only columns that contain numbers
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data_copy4 = data_copy3.copy()
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for column in data_copy4:
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if isinstance(data_copy4[column][0], str):
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del data_copy4[column]
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print(data_copy4)
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return data_copy4
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def graph_plot():
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data = get_data()
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sns.set_style("whitegrid")
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plt.figure(figsize=(15, 10))
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sns.heatmap(data.corr())
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plt.savefig("plot1.png")
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# plt.show()
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# korealācija novērojama starp kolonnām [length,width,wheel-base] un [engine-size,price,horsepower]
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# noderīga ir otrā korelācija, jo tā atklāj to savstarpējo ietekmi
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# matplotlib heatmap veido korealāciju starp datiem savstarpēji salīdzinot to vērtības un norādot iegūtos koeficientus
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# seaborn heatmap veido korealāciju starp datu vērtībām pēc pašnoteiktas korealācijas skalas
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sns.displot(data["price"])
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plt.savefig("plot2.png")
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# plt.show()
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plt.scatter(data["price"], data["engine-size"])
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plt.savefig("plot3.png")
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# plt.show()
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sns.scatterplot(data["price"], data["engine-size"])
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plt.savefig("plot4.png")
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# plt.show()
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if __name__ == '__main__':
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# get_data()
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graph_plot()
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