id int64 0 7.95k | code_block stringlengths 1 52.9k | too_long stringclasses 2
values | marks int64 1 5 | graph_vertex_id float64 1 88 | graph_vertex_class stringclasses 12
values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3,818 | sns.barplot(data=train, x="datetime-second", y="count") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,819 | # Integer String .
train["datetime-year_month"] = train["datetime-year"].astype(str) + "-" + train["datetime-month"].astype(str)
print(train.shape)
train.info()' | Yes | 3 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,820 | train[["datetime-year", "datetime-month", "datetime-year_month"]] | No | 5 | 41 | Debug | show_table | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,821 | sns.barplot(data=train, x="datetime-year_month", y="count") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,822 | # !
import matplotlib.pyplot as plt
plt.figure(figsize=(24,4))
sns.barplot(data=train, x="datetime-year_month", y="count")' | Yes | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,823 | plt.figure(figsize=(24,4))
sns.pointplot(data=train, x="datetime-hour", y="count") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,824 | plt.figure(figsize=(24,4))
sns.pointplot(data=train, x="datetime-hour", y="count", hue="workingday") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,825 | plt.figure(figsize=(24,4))
sns.pointplot(data=train, x="datetime-hour", y="count", hue="datetime-dayofweek") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,826 | # x_train .
x_train = train[feature_names]
# x_train .
print(x_train.shape)
# x_test 5 .
x_train.head()' | Yes | 4 | 41 | Debug | show_table | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,827 | # x_test .
x_test = test[feature_names]
# x_test .
print(x_test.shape)
# x_test 5 .
x_test.head()' | Yes | 4 | 41 | Debug | show_table | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,828 | # .
label_name = "count"
y_train = train[label_name]
print(y_train.shape)
y_train.head()' | Yes | 4 | 58 | Debug | show_shape | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,830 | from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
model | Yes | 5 | 4 | Model_Train | choose_model_class | 2,719,562 | 3 | C | 2 | 0 | 0 | 1 | 10 | 3 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Randomness Control Checker", "fix": "Set random_state in estimators for reproducibility.", "benefits": "Ensures reproducible results and consistent model behaviour.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "ScikitLe... | B | Randomness Control Checker=low | [{"type": "Randomness Control Checker", "severity": "low"}] | ["Verbose Mode Checker"] | 3 | C |
3,831 | from sklearn.model_selection import cross_val_predict
y_predict = cross_val_predict(model, x_train, y_train, cv=20)
print(y_predict.shape)
y_predict | Yes | 4 | 27 | Model_Train | predict_on_train | 2,719,562 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
3,832 | score = abs(y_train - y_predict).mean()
f"score(Mean Absolute Error)={score:.6f}" | No | 5 | 28 | Model_Train | compute_train_metric | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | B |
3,833 | # .
model.fit(x_train, y_train)' | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,834 | # x_test .
predictions = model.predict(x_test)
# array .
predictions' | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,835 | #
predictions[:5]' | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,836 | submit = pd.read_csv("../input/sampleSubmission.csv")
print(submit.shape)
submit.head() | Yes | 3 | 45 | Data_Extraction | load_from_csv | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/sampleSubmission.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
3,837 | submit["count"] = predictions
print(submit.shape)
submit.head() | Yes | 3 | 41 | Debug | show_table | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 3 | A |
3,838 | submit.to_csv("baseline-script.csv", index=False)
pd.read_csv("baseline-script.csv").head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}] | A | no applicable ML-specific findings | [] | ["No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 3 | A |
3,839 | df_train = pd.read_csv('../input/train.csv')
df_train.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,840 | dtIdx = pd.DatetimeIndex(df_train['datetime']) | No | 5 | 16 | Data_Transform | data_type_conversions | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 9 | A |
3,841 | df_train['hour'] = dtIdx.hour
df_train['dayofweek'] = dtIdx.dayofweek
df_train['month'] = dtIdx.month
df_origin = df_train | No | 4 | 16 | Data_Transform | data_type_conversions | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 9 | A |
3,842 | df_train = df_train.drop(['casual', 'registered', 'datetime'], axis = 1)
df_train.head() | Yes | 5 | 10 | Data_Transform | drop_column | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 9 | B |
3,843 | df_train_data = df_train.drop('count', axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 9 | B |
3,844 | df_train_target = df_train['count'] | No | 2 | 13 | Data_Transform | split | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 9 | A |
3,845 | df_train_target.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,846 | from sklearn import linear_model
from sklearn import svm
from sklearn.ensemble import RandomForestRegressor
from sklearn import model_selection | No | 5 | 22 | Environment | import_modules | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,847 | ms = model_selection.ShuffleSplit(n_splits=3, test_size=0.2, random_state=0) | No | 5 | 13 | Data_Transform | split | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,851 | df_train_data.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,854 | X_train, X_test, y_train, y_test = model_selection.\\
train_test_split(df_train_data, df_train_target, test_size = 0.2, random_state=0)' | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,855 | \ | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,856 | import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the te... | Yes | 4 | 35 | Visualization | learning_history | null | 1 | A | 1 | 2 | 1 | 0 | 5 | 29 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 0 | A |
3,858 | df_origin.columns | No | 5 | 71 | Debug | show_columns | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,859 | df_origin.groupby('windspeed').mean().plot(y='count', marker='o') | No | 3 | 33 | Visualization | distribution | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,860 | df_origin.groupby('humidity').mean().plot(y='count', marker='o') | No | 4 | 33 | Visualization | distribution | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,861 | d = df_origin.groupby('humidity') | No | 5 | 60 | Data_Transform | groupby | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,862 | corr = df_origin[['temp','weather','windspeed','dayofweek', 'month', 'hour','count']].corr()
corr | No | 5 | 40 | Debug | show_table_attributes | 2,874,959 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,863 | import matplotlib.pyplot as plt
plt.figure()
plt.matshow(corr)
plt.colorbar()
plt.show() | Yes | 5 | 80 | Visualization | heatmap | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,864 | df_test = pd.read_csv('../input/test.csv')
df_test.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,865 | df_sample = pd.read_csv('../input/sampleSubmission.csv')
df_sample.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/sampleSubmission.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | A |
3,866 | df_test['hour'] = pd.DatetimeIndex(df_test['datetime']).hour
df_test['dayofweek'] = pd.DatetimeIndex(df_test['datetime']).dayofweek
df_test['month'] = pd.DatetimeIndex(df_test['datetime']).month
df_test_data = df_test.drop(['datetime'], axis=1)
df_test_data.head() | Yes | 4 | 8 | Data_Transform | feature_engineering | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 9 | A |
3,867 | score = rfr.score(df_train_data, df_train_target) | No | 5 | 28 | Model_Train | compute_train_metric | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 9 | A |
3,868 | print("score: %.3f"%score) | No | 5 | 84 | Other | not_enough_vertices | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,869 | df_sample.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,871 | df_sample.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,872 | df_sample.to_csv('submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,873 | df_demo = pd.read_csv('submission.csv')
df_demo.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 2,874,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,874 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
print(os.listdir("../input"))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from sklearn import datasets, linear_model
from sklearn.metrics... | Yes | 3 | 45 | Data_Extraction | load_from_csv | 3,335,717 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 7 | null |
3,875 | print(test.head(5))
| No | 5 | 41 | Debug | show_table | 3,335,717 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
3,876 | # Extract hours from datetime
data['datetime'] = pd.to_datetime(data['datetime'])
data['hour'] = data['datetime'].dt.hour
data['month'] = data['datetime'].dt.month
test['datetime'] = pd.to_datetime(test['datetime'])
test['hour'] = data['datetime'].dt.hour
test['month'] = data['datetime'].dt.month
data['season'] = dat... | No | 3 | 16 | Data_Transform | data_type_conversions | 3,335,717 | 1 | A | 0 | 0 | 0 | 0 | 0 | 19 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
3,877 | data = data.drop(['atemp', 'casual', 'registered', 'windspeed'], axis=1)
test = test.drop(['atemp','windspeed'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 3,335,717 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
3,878 | test.head(2) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,879 | import math
data['count'] = data['count'].transform(lambda x: math.log(x)) | Yes | 5 | 8 | Data_Transform | feature_engineering | 3,335,717 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 7 | A |
3,880 | data = data.drop(['datetime'], axis=1)
data_dummy = data
#test = test.drop(['datetime'], axis=1)
test_dummy = test
def dummify_dataset(df, column):
df = pd.concat([df, pd.get_dummies(df[column], prefix=column, drop_first=True)],axis=1)
df = df.drop([column], axis=1)
return df
columns_to_dummify = ... | Yes | 5 | 20 | Data_Transform | categorify | 3,335,717 | 1 | A | 1 | 2 | 1 | 0 | 5 | 12 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: dummify_dataset. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 14"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 7 | A |
3,881 | from sklearn.model_selection import train_test_split
y = data_dummy['count']
X = data_dummy.drop(['count'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=42)
| Yes | 4 | 21 | Data_Transform | prepare_x_and_y | 3,335,717 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 7 | B |
3,882 | from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor, ElasticNetCV
from sklearn.metrics import mean_squared_log_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.ensemble import BaggingRegr... | Yes | 5 | 7 | Model_Train | train_model | null | 1 | A | 2 | 0 | 0 | 0 | 10 | 8 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | B |
3,883 | test_with_datetime = pd.read_csv("../input/test.csv")
test_dummy = test_dummy.drop(['datetime'], axis=1)
test_predictions = etr.predict(test_dummy)' | Yes | 3 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,884 | predictions = np.exp(test_predictions )
submission = pd.DataFrame({ 'datetime': test.datetime.values, 'count': predictions })
submission.to_csv("my_submission_10.csv", index=False)' | Yes | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,885 | train = pd.read_csv('../input/train.csv')
train.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,886 | test = pd.read_csv('../input/test.csv')
test.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,887 | train = pd.read_csv("../input/train.csv", parse_dates = ["datetime"])
test = pd.read_csv("../input/test.csv", parse_dates = ["datetime"]) | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,888 | train.dtypes | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,889 | train["year"] = train["datetime"].dt.year
train["hour"] = train["datetime"].dt.hour
train["dayofweek"] = train["datetime"].dt.dayofweek
test["year"] = test["datetime"].dt.year
test["hour"] = test["datetime"].dt.hour
test["dayofweek"] = test["datetime"].dt.dayofweek | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,890 | train.drop(["datetime", "windspeed", "casual", "registered", "count"], 1, inplace=True)
test.drop(["datetime", "windspeed"], 1, inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 3,475,398 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 4"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["InPlace Checker"] | 3 | B |
3,891 | from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=100)
rf.fit(train,y_train)
preds = rf.predict(test) | Yes | 4 | 7 | Model_Train | train_model | 3,475,398 | 2 | B | 2 | 0 | 0 | 0 | 10 | 4 | ok | 10 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
3,892 | submission = pd.read_csv('../input/sampleSubmission.csv')
submission.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,893 | submission["count"] = np.expm1(preds)
submission.head() | Yes | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,894 | submission.to_csv("allrf.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,895 | sample=pd.read_csv('../input/sampleSubmission.csv')
train_df=pd.read_csv('../input/train.csv')
test_df=pd.read_csv('../input/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 4 | A |
3,896 | sample.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,897 | train_df.nunique() | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,898 | test_df.nunique() | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,899 | train_df.describe() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,900 | season_df=train_df.groupby('season') | No | 5 | 60 | Data_Transform | groupby | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 4 | B |
3,901 | season_df.head() | No | 5 | 41 | Debug | show_table | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,902 | train_df['hour']=hour
train_df['day']=day
train_df['month']=month
train_df['year']=year | No | 2 | 16 | Data_Transform | data_type_conversions | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,903 | hour=[]
day=[]
month=[]
year=[]
for row in test_df['datetime']:
date_hour=row.split()
date=date_hour[0]
hour_row=date_hour[1]
hour.append(hour_row.split(':')[0])
date=date.split('-')
day.append(date[2])
month.append(date[1])
year.append(date[0])
test_df['hour']=hour
test_df['day']=day
te... | No | 5 | 8 | Data_Transform | feature_engineering | 3,652,538 | 1 | A | 0 | 1 | 1 | 0 | 0 | 17 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,904 | datetime=['hour','day','month','year']
for time in datetime:
train_df[time]=train_df[time].astype(int)
test_df[time]=test_df[time].astype(int) | No | 4 | 16 | Data_Transform | data_type_conversions | 3,652,538 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,905 | #Continous Features Analysis
for i in range(len(cont_feat)-1):
for j in range(i+1,len(cont_feat)):
sns.jointplot(cont_feat[i],cont_feat[j],data=train_df)
plt.title('{} relation with {}'.format(cont_feat[i],cont_feat[j]))
plt.show()
| No | 5 | 33 | Visualization | distribution | 3,652,538 | 1 | A | 0 | 2 | 3 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,906 | #Categorical feature analysis
for cat in cat_feat:
sns.barplot(x=cat,y='count',data=train_df,estimator=sum)
plt.title('{} vs total_rent'.format(cat))
plt.show() | No | 4 | 81 | Visualization | relationship | 3,652,538 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,907 | climate=['temp','humidity','windspeed']
for clim in climate:
sns.swarmplot(x='hour',y=clim,hue='season',data=train_df)
plt.title('{} vs {}'.format('hour',clim))
plt.show() | No | 5 | 33 | Visualization | distribution | 3,652,538 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,908 | sns.distplot(train_df['count'])
train_df['count']=train_df['count'].apply(lambda x:np.log(x)) | No | 4 | 33 | Visualization | distribution | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,909 | sns.heatmap(train_df.corr()) | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,910 | train_df=pd.DataFrame(train_df) | No | 5 | 12 | Data_Transform | create_dataframe | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,911 | train_df.set_index('datetime',inplace=True) | No | 5 | 61 | Data_Transform | rename_columns | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 4 | B |
3,912 | test_df.set_index('datetime',inplace=True) | No | 4 | 61 | Data_Transform | rename_columns | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,913 | train_df.drop(columns=['casual','registered'],axis=1,inplace=True) | No | 4 | 61 | Data_Transform | rename_columns | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 5"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Chec... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["InPlace Checker"] | 4 | B |
3,914 | weather_df=pd.get_dummies(train_df['weather'],prefix='weather')
yr_df=pd.get_dummies(train_df['year'],prefix='year')
month_df=pd.get_dummies(train_df['month'],prefix='month')
hour_df=pd.get_dummies(train_df['hour'],prefix='hour')
season_df=pd.get_dummies(train_df['season'],prefix='season')
train_df=train_df.join(weathe... | No | 4 | 20 | Data_Transform | categorify | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 20 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,915 | train_df.drop(columns=['season','hour','month','year','weather'],axis=1,inplace=True)
test_df.drop(columns=['season','hour','month','year','weather'],axis=1,inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 5"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["InPlace Checker"] | 4 | B |
3,916 | def rmlse(predicted,actual):
sum_val=0
for i in range(len(predicted)):
sum_val+=(np.log(predicted[i]+1)-np.log(actual[i]+1))**2
return (sum_val/len(predicted))**(0.5) | No | 5 | 84 | Other | not_enough_vertices | 3,652,538 | 1 | A | 0 | 2 | 1 | 0 | 0 | 5 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Missing function docstring"] | 4 | A |
3,917 | X=train_df.drop(columns='count',axis=1)
y=train_df['count'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,918 | X.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,919 | from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)
'''
param_test1 = {
'max_depth':range(3,10,2),
'min_child_weight':range(1,6,2)
}
'''
'''
param_test2... | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | 3,652,538 | 1 | A | 4 | 0 | 0 | 0 | 20 | 36 | ok | 7 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 33"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | A |
3,920 | y_test | Yes | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,921 | import xgboost as xgb
xgr=xgb.XGBRegressor(learning_rate =0.1, n_estimators=1000, max_depth=9,
min_child_weight=4, gamma=0.125, subsample=1, colsample_bytree=0.8)
xgr.fit(train_df.drop(columns='count',axis=1),train_df['count'])
y_predict=xgr.predict(test_df) | No | 4 | 7 | Model_Train | train_model | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 9 | 8 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
3,922 | test_df['count']=np.exp(y_predict) | Yes | 5 | 8 | Data_Transform | feature_engineering | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,923 | result=pd.DataFrame() | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,924 | result['datetime']=test_df['datetime']
result['count']=test_df['count'] | No | 5 | 55 | Data_Export | prepare_output | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,925 | result.to_csv('output.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 3,652,538 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
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