Solución Lab2 13/11/24

functions.py

@@ -0,0 +1,90 @@
+def merge_df(df1,df2):
+    import pandas as pd
+    df=df1.merge(df2,how="outer")
+    return df
+
+def improve_title_columns(df):
+    import pandas as pd
+    df=df.rename(columns={df.columns[n]:df.columns[n].strip().replace(" ","_").lower() for n in range(len(df.columns))})
+    #rename column st to state
+    df=df.rename(columns={"st":"state"})
+    df.columns
+    return df
+
+def data_standarization(df):
+    import pandas as pd
+
+    state_values={ #creates a dictionary with the values and corrections
+        "Oregon":"Oregon",
+        "California":"California",
+        "Cali":"California",
+        "Arizona":"Arizona",
+        "AZ":"Arizona",
+        "Washington":"Washington",
+        "WA":"Washington",
+        "Nevada":"Nevada",
+        }
+    df["State"]=df["State"].replace(state_values) #replace with correct values
+    """
+    gender_values={ #creates a dictionary with the values and corrections
+    "F":"F",
+    "Femal":"F",
+    "female":"F",
+    "Male":"M"}
+
+    state_values={ #creates a dictionary with the values and corrections
+        "Oregon":"Oregon",
+        "California":"California",
+        "Cali":"California",
+        "Arizona":"Arizona",
+        "AZ":"Arizona",
+        "Washington":"Washington",
+        "WA":"Washington",
+        "Nevada":"Nevada",
+        }
+
+    education_values={ #creates a dictionary with the values and corrections
+        "Bachelors":"Bachelor"
+            }
+
+    vehicle_class_values={ #creates a dictionary with the values and corrections
+        "Sports Car":"Luxury",
+        "Luxury SUV":"Luxury",
+        "Luxury Car":"Luxury",
+        }
+
+    df["gender"]=df["gender"].replace(gender_values)
+    df["state"]=df["state"].replace(state_values) #replace with correct values 
+    df["state"]=df["state"].replace(state_values) #replace with correct values 
+    df["vehicle_class"]=df["vehicle_class"].replace(vehicle_class_values) #replace with correct values 
+
+    #Replace % caracter with none in customer_lifetime_value 
+    df["customer_lifetime_value"]=df["customer_lifetime_value"].str.replace("%","")
+    """
+    #Cleaning NaN and null values}
+
+    #First cleaning
+
+    datos_iniciales=df.shape[0] #valor del total de filas antes de limpieza
+    df=df.dropna(how="all")
+    df.fillna(0, inplace=True)
+    datos_finales=df.shape[0]
+    '''
+    #complains open format manage
+    list_complains_types=df["number_of_open_complaints"].unique()
+    list_complains_types=list(list_complains_types)
+    list_complains=[list_complains_types[n][2].split("/") for n in range(len(list_complains_types))]
+    dict_complains=dict(zip(list_complains_types,list_complains))
+
+    df["number_of_open_complaints"]=df["number_of_open_complaints"].replace(dict_complains)
+
+    #changing data type
+    df["vehicle_class"]=df["vehicle_class"].astype("object")
+    df["customer_lifetime_value"]=df["customer_lifetime_value"].astype("float64")
+    df["number_of_open_complaints"]=df["number_of_open_complaints"].astype(int)
+    '''
+    print(f"Data before cleaning: {datos_iniciales}\n Data after cleaning: {datos_finales}")
+
+
+
+    return df