Classwork 190122

january/task_050122/main_vs_module_A_Bisenieks.py

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+import pandas
+
+dati = pandas.read_csv('auto_imports_mainits.csv')
+
+for index, element in enumerate(dati.isnull().sum()):
+	if element != 0: print(f"|  {dati.columns[index]}" + " " * (25 - len(str(dati.columns[index]))) + f"{element}")

january/task_050122/task_190122.py

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+# Author - Kristiāns Francis Cagulis
+# Date - 19.01.2022
+# Title - Classwork
+
+import pandas as pd
+from word2number import w2n
+
+
+def main():
+	data = pd.read_csv("auto_imports_mainits.csv")
+
+	# summary = data["normalized-losses"].notnull()  # returns boolean
+	# print(data[summary], "\n") # "normalized-losses" is not empty
+	# print(data[~summary])  # inverts all the bits
+	# print(len(data[~summary]))
+
+	data_copy = data.copy()
+
+	# Delete rows with empty spots
+	# print(f"Before erasing: {data_copy.shape}")
+	# print(f"After erasing: {data_copy.dropna().shape}")
+
+	# Delete column
+	# print(f"Before erasing: {data_copy.shape}")
+	del data_copy["normalized-losses"]
+	# print(f"After erasing: {data_copy.shape}")
+	# print(f"Blank spots: {data_copy.isnull().any().sum()}")
+
+	# data_copy2 = data_copy.copy()
+	# print(data_copy2.head())
+	# data_copy2.drop(data_copy2.columns[[0, 1]], axis=1, inplace=True)
+	# print(data_copy2.head())
+
+	dislike = ["N/A", "NA", "--"]
+	data_copy3 = pd.read_csv("auto_imports_mainits.csv", na_values=dislike)
+
+	# Mean
+	# print(data_copy3.iloc[52], "\n")
+	# mean = data_copy3["bore"].mean()
+	# data_copy3["bore"].fillna(mean, inplace=True)
+	# print(data_copy3.iloc[52])
+
+	# Median
+	# print(data_copy3.iloc[53], "\n")
+	# median = data_copy3["bore"].median()
+	# data_copy3["bore"].fillna(median, inplace=True)
+	# print(data_copy3.iloc[53])
+
+	# Mode
+	# print(data_copy3.iloc[60], "\n")
+	# mode = data_copy3["bore"].mode()
+	# data_copy3["bore"].fillna(mode, inplace=True)
+	# print(data_copy3.iloc[60])
+
+	# print(data_copy3.dtypes)
+
+	# data_copy3["curb-weight"] = pd.to_numeric(data_copy3["curb-weight"], errors='coerce')
+	# data_copy3["curb-weight"] = data_copy3["curb-weight"].astype("float64")
+	# print(data_copy3.dtypes)
+
+	# Replaces word written numbers to intigers
+	columns = ["num-of-doors", "num-of-cylinders"]
+	for column in columns:
+		for value in data_copy3[column]:
+			try:
+				data_copy3 = data_copy3.replace(to_replace=value, value=w2n.word_to_num(value))
+				print(type(w2n.word_to_num(value)))
+			except:
+				pass
+	print(data_copy3[["num-of-doors", "num-of-cylinders"]])
+
+	# Leaves only columns that contain numbers
+	data_copy4 = data_copy3.copy()
+	for column in data_copy4:
+		if isinstance(data_copy4[column][0], str):
+			del data_copy4[column]
+	print(data_copy4)
+
+
+if __name__ == '__main__':
+	main()