View apriori.sql
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with base as | |
( | |
SELECT * | |
FROM Product_Details_002 | |
where Product_ID is not null | |
), | |
inner_joined_cte as | |
( | |
select a. Order_ID, a. Product_ID as puller_Art, b. Product_ID as pulled_art | |
from base as a |
View bus_price_clean_5.py
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f = final_cleaned_df.copy() | |
b1 = f[(f["Bus"] == "a6951a59b64579edcf822ab9ea4c0c83") & (f["Service_Date"] == "15-07-2020 00:00")] | |
b2 = f[(f["Bus"] == "ab479dab4a9e6bc3eaefe77a09f027ed") & (f["Service_Date"] == "15-07-2020 00:00")] | |
recorded_dates_df = pd.concat([b1[["RecordedAt_new"]], b2[["RecordedAt_new"]]], axis = 0).drop_duplicates().sort_values(by = "RecordedAt_new").reset_index().drop(columns = "index") | |
joined_1 = pd.merge(recorded_dates_df, b1, on=["RecordedAt_new"], how='left',suffixes=('_actuals', '_B1')) | |
joined_df = pd.merge(joined_1, b2, on=["RecordedAt_new"], how='left',suffixes=('_B1', '_B2')) | |
joined_df |
View bus_price_clean_4.py
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figure(figsize=(10, 6), dpi=80) | |
price_f = final_cleaned_df[final_cleaned_df["average_price_s1_s2_filled"] != 0] | |
plt.subplot(1, 2, 1) | |
plt.title("Boxplot - Average Price") | |
price_f = final_cleaned_df[final_cleaned_df["average_price_s1_s2_filled"] != 0] | |
plt.boxplot(price_f.average_price_s1_s2_filled) | |
plt.subplot(1, 2, 2) | |
plt.title("Histogram - Average Price") |
View bus_price_clean_3.py
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plot_1 = bus_fare_df[bus_fare_df["average_price_s1_s2_filled"] !=0].groupby(["RecordedAt_date_only"]).agg({"average_price_s1_s2_filled":np.mean}) | |
figure(figsize=(10, 6), dpi=80) | |
plt.plot(plot_1.index, plot_1.average_price_s1_s2_filled,label = "Platform") | |
plot_2 = bus_fare_df[(bus_fare_df["average_price_s1_s2_filled"] !=0)&(bus_fare_df["Bus"] =="060c6d5595f3d7cf53838b0b8c66673d")].groupby(["RecordedAt_date_only"]).agg({"average_price_s1_s2_filled":np.mean}) | |
plt.plot(plot_2.index, plot_2.average_price_s1_s2_filled, label = "060c6d5595f3d7cf53838b0b8c66673d") | |
plt.show() |
View bus_price_clean_2.py
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groups = final_cleaned_df.groupby(["Bus","Service_Date_new"]).RecordedAt_new | |
min_val = groups.transform(min) | |
one_df = final_cleaned_df[(final_cleaned_df.RecordedAt_new==min_val) ] | |
one_df["date_diff"] = one_df.Service_Date_new - one_df.RecordedAt_new | |
figure(figsize=(10, 6), dpi=80) | |
plt.subplot(1, 2, 1) | |
plt.title("Date Difference Boxplot") | |
plt.boxplot(one_df.date_diff.astype('timedelta64[D]')) | |
plt.subplot(1, 2, 2) | |
plt.title("Date Difference Histogram") |
View bus_price_clean_1.py
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one_df_002 = pd.merge(one_df,final_cleaned_df[["Bus","Service_Date","RecordedAt", "average_price_s1_s2_filled"]], how = "left" , left_on = ["Bus","Service_Date","min"], right_on =["Bus","Service_Date","RecordedAt"], | |
suffixes=('_left', '_right')) | |
one_df_003 = pd.merge(one_df_002,final_cleaned_df[["Bus","Service_Date","RecordedAt", "average_price_s1_s2_filled"]], how = "left" , left_on = ["Bus","Service_Date","max"], right_on =["Bus","Service_Date","RecordedAt"], | |
suffixes=('_left', '_right')) | |
one_df_003["price_diff_i_f"] = one_df_003.average_price_s1_s2_filled_right - one_df_003.average_price_s1_s2_filled_left | |
one_df_003["price_diff_i_f_perc"] = one_df_003.price_diff_i_f / one_df_003.average_price_s1_s2_filled_left | |
one_df_004 = one_df_003[["Bus","Service_Date", "price_diff_i_f"]].drop_duplicates() | |
one_df_005 = one_df_003[["Bus","Service_Date", "price_diff_i_f_perc"]].drop_duplicates() | |
one_df_005.boxplot(column = ["price_diff_i_f_perc"]) | |
one_df_004.price_diff_i_f.hist( |
View bus_price_clean_002.py
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bus_fare_df = bus_fare_df.sort_values(by = ["Bus","Service_Date","RecordedAt" ]) | |
# display(bus_fare_df.head()) | |
test = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_1_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) | |
test = test[["Bus","Service_Date","Seat_Fare_Type_1_average" ]] | |
test["Seat_Fare_Type_1_average_impute"] = test.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='ffill')) | |
display(test.shape) | |
display(bus_fare_df.shape) | |
test2 = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_2_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) | |
test2 = test2[["Bus","Service_Date","Seat_Fare_Type_2_average" ]] |
View bus_price_clean.py
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def clean_seat(x): | |
''' | |
input is a string object and not a list | |
''' | |
# a = [float(sing_price) for price in x for sing_price in price.split(",")] | |
# a = [sing_price for price in x for sing_price in price.split(",")] | |
# return sum(a)/len(a) | |
a = [float(price) for price in x.split(",")] | |
return sum(a)/len(a) |
View lab_utils_cls.py
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import statistics | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
from sklearn.utils.multiclass import unique_labels | |
from sklearn.metrics import confusion_matrix | |
import os | |
import tempfile | |
import numpy as np |
View catboost.py
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class catboost_regressor(): | |
''' | |
''' | |
def __init__(self, param = []): | |
''' | |
''' | |
self._rfr = CatBoostRegressor(**params) ## kwargs loss_function='RMSE', iterations = 100 | |
self._param = param |
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