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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
72068232/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts() | code |
105200230/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-2020/checkout.csv'
bank = pd.read_csv(BANK_FILE)
checkout = pd.read_csv(CHECKOUT_FILE)
print(bank.columns)
print(checkout.columns) | code |
105200230/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-2020/checkout.csv'
bank = pd.read_csv(BANK_FILE)
checkout = pd.read_csv(CHECKOUT_FILE)
print(len(bank))
print(len(checkout))
bank = bank.sort_values(by=['stmt_amount'], ascending=True)
checkout = checkout.sort_values(by=['ckt_amount'], ascending=True) | code |
105200230/cell_5 | [
"text_plain_output_1.png"
] | from fuzzywuzzy import fuzz
from tqdm.notebook import tqdm
import csv
import pandas as pd
import csv
import numpy as np
import re
import pandas as pd
from tqdm.notebook import tqdm
from fuzzywuzzy import fuzz
BANK_FILE = '../input/payment-id-ndsc-2020/bank_statement.csv'
CHECKOUT_FILE = '../input/payment-id-ndsc-2020/checkout.csv'
bank = pd.read_csv(BANK_FILE)
checkout = pd.read_csv(CHECKOUT_FILE)
bank = bank.sort_values(by=['stmt_amount'], ascending=True)
checkout = checkout.sort_values(by=['ckt_amount'], ascending=True)
grouped_bank = bank.groupby('stmt_amount')
grouped_checkout = checkout.groupby('ckt_amount')
amount_list = list(set(bank['stmt_amount'].tolist()))
len(amount_list)
amount_list.sort()
local_bank = bank
local_checkout = checkout
pair_list = []
paired_data = []
pbar = tqdm(total=len(bank))
skipped_amounts = []
matchless = []
matchless_count = 0
for amount in amount_list:
bank_group = grouped_bank.get_group(amount)
checkout_group = grouped_checkout.get_group(amount)
skip_list = []
matchless_count += len(checkout_group)
matchless_min_score = 120
while len(checkout_group) > 0 or len(bank_group) > 0:
matchless_min_score -= 20
for bank_index, bank_row in bank_group.iterrows():
current = bank_index
for checkout_index, checkout_row in checkout_group.iterrows():
pbar.desc = 'Purge {} {} With {} data {} miss'.format(matchless_min_score, amount, len(bank_group), len(checkout_group))
fuzz_result = fuzz.token_set_ratio(checkout_row['buyer_name'], bank_row['desc'])
if fuzz_result >= matchless_min_score:
pbar.update(1)
checkout_group = checkout_group.drop(checkout_index)
bank_group = bank_group.drop(bank_index)
paired_data.append([bank_row['stmt_id'], checkout_row['ckt_id']])
matchless_count -= 1
break
with open('paired_data_long.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['stmt_id', 'ckt_id'])
writer.writerows(paired_data) | code |
18100538/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True}) | code |
18100538/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.head() | code |
2036992/cell_42 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
ET.fit(X_train, Y_train) | code |
2036992/cell_21 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
df_train.head()
X_train.head() | code |
2036992/cell_23 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
df_train_stdrop.head() | code |
2036992/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
GB.fit(X_train, Y_train) | code |
2036992/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df_train.head() | code |
2036992/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
RF.fit(X_train, Y_train) | code |
2036992/cell_29 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
LR = linear_model.LinearRegression()
LR.fit(X_train, Y_train)
lr_score = LR.score(X_test, Y_test)
print('Linear regression processing ,,,')
print('Linear regression Score: %.2f %%' % lr_score) | code |
2036992/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
AB.fit(X_train, Y_train)
AB_feature = AB.feature_importances_
AB_feature
ab_score = AB.score(X_test, Y_test)
print('AdaBoostClassifier processing ,,,')
print('AdaBoostClassifier Score: %.3f %%' % ab_score) | code |
2036992/cell_41 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
RF.fit(X_train, Y_train)
RF_feature = RF.feature_importances_
RF_feature
rf_score = RF.score(X_test, Y_test)
print('RandomForestClassifier processing ,,,')
print('RandomForestClassifier Score: %.3f %%' % rf_score) | code |
2036992/cell_7 | [
"text_html_output_1.png"
] | df_test.head() | code |
2036992/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
GB.fit(X_train, Y_train)
GB_feature = GB.feature_importances_
GB_feature
gb_score = GB.score(X_test, Y_test)
print('GradientBoostingClassifier processing ,,,')
print('GradientBoostingClassifier Score: %.3f %%' % gb_score) | code |
2036992/cell_49 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
AB.fit(X_train, Y_train)
AB_feature = AB.feature_importances_
AB_feature
ab_score = AB.score(X_test, Y_test)
RF.fit(X_train, Y_train)
RF_feature = RF.feature_importances_
RF_feature
rf_score = RF.score(X_test, Y_test)
ET.fit(X_train, Y_train)
ET_feature = ET.feature_importances_
ET_feature
et_score = ET.score(X_test, Y_test)
GB.fit(X_train, Y_train)
GB_feature = GB.feature_importances_
GB_feature
gb_score = GB.score(X_test, Y_test)
cols = X_train.columns.values
feature_df = pd.DataFrame({'features': cols, 'AdaBoost': AB_feature, 'RandomForest': RF_feature, 'ExtraTree': ET_feature, 'GradientBoost': GB_feature})
feature_df.head(8) | code |
2036992/cell_18 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
std_X_train.nsmallest(10, columns=0).head(10) | code |
2036992/cell_51 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
AB.fit(X_train, Y_train)
AB_feature = AB.feature_importances_
AB_feature
ab_score = AB.score(X_test, Y_test)
RF.fit(X_train, Y_train)
RF_feature = RF.feature_importances_
RF_feature
rf_score = RF.score(X_test, Y_test)
ET.fit(X_train, Y_train)
ET_feature = ET.feature_importances_
ET_feature
et_score = ET.score(X_test, Y_test)
GB.fit(X_train, Y_train)
GB_feature = GB.feature_importances_
GB_feature
gb_score = GB.score(X_test, Y_test)
cols = X_train.columns.values
feature_df = pd.DataFrame({'features': cols, 'AdaBoost': AB_feature, 'RandomForest': RF_feature, 'ExtraTree': ET_feature, 'GradientBoost': GB_feature})
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
graph = feature_df.plot.bar(figsize=(18, 10), title='Feature distribution', grid=True, legend=True, fontsize=15, xticks=feature_df.index)
graph.set_xticklabels(feature_df.features, rotation=80) | code |
2036992/cell_28 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
LR = linear_model.LinearRegression()
LR.fit(X_train, Y_train) | code |
2036992/cell_16 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
Y_train.describe() | code |
2036992/cell_38 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
AB.fit(X_train, Y_train) | code |
2036992/cell_43 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
Y_train['xAttack'] = le.fit_transform(Y_train['xAttack'])
lb.fit_transform(Y_train['xAttack'])
Y_test['xAttack'] = le.fit_transform(Y_test['xAttack'])
lb.fit_transform(Y_test['xAttack'])
con_list = ['protocol_type', 'service', 'flag', 'land', 'logged_in', 'su_attempted', 'is_host_login', 'is_guest_login']
con_train = X_train.drop(con_list, axis=1)
stdtrain = con_train.std(axis=0)
std_X_train = stdtrain.to_frame()
X_train = X_train.drop(['num_outbound_cmds'], axis=1)
X_test = X_test.drop(['num_outbound_cmds'], axis=1)
df_train = pd.concat([X_train, Y_train], axis=1)
stdrop_list = ['urgent', 'num_shells', 'root_shell', 'num_failed_logins', 'num_access_files', 'dst_host_srv_diff_host_rate', 'diff_srv_rate', 'dst_host_diff_srv_rate', 'wrong_fragment']
X_test_stdrop = X_test.drop(stdrop_list, axis=1)
X_train_stdrop = X_train.drop(stdrop_list, axis=1)
df_train_stdrop = pd.concat([X_train_stdrop, Y_train], axis=1)
AB = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, learning_rate=1.0)
RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='auto', bootstrap=True)
ET = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_features='auto', bootstrap=False)
GB = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=200, max_features='auto')
y_train = Y_train['xAttack'].ravel()
x_train = X_train.values
x_test = X_test.values
ET.fit(X_train, Y_train)
ET_feature = ET.feature_importances_
ET_feature
et_score = ET.score(X_test, Y_test)
print('ExtraTreesClassifier processing ,,,')
print('ExtraTreeClassifier: %.3f %%' % et_score) | code |
2036992/cell_14 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
X_train = df_train.drop('xAttack', axis=1)
Y_train = df_train.loc[:, ['xAttack']]
X_test = df_test.drop('xAttack', axis=1)
Y_test = df_test.loc[:, ['xAttack']]
le = preprocessing.LabelEncoder()
enc = OneHotEncoder()
lb = preprocessing.LabelBinarizer()
X_train['protocol_type'] = le.fit_transform(X_train['protocol_type'])
X_test['protocol_type'] = le.fit_transform(X_test['protocol_type'])
X_train.head() | code |
1005471/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
def get_value_counts(col, df):
return pd.DataFrame(df[col].value_counts())
global_bar_chart_settings = {'height': 4, 'width': 8, 'bar_width': 0.9, 'title': 'Number of occurrences of ', 'ylabel': 'Occurrence', 'alpha': None, 'lbl_fontsize': 15, 'title_fontsize': 20}
def plot_bar(chart_settings, df, column):
width = global_bar_chart_settings['width']
height = global_bar_chart_settings['height']
alpha = global_bar_chart_settings['alpha']
title = global_bar_chart_settings['title']
bar_width = global_bar_chart_settings['bar_width']
ylabel = global_bar_chart_settings['ylabel']
lbl_fontsize = global_bar_chart_settings['lbl_fontsize']
title_fontsize = global_bar_chart_settings['title_fontsize']
chart_keys = chart_settings.keys()
if 'width' in chart_keys:
width = chart_settings['width']
if 'height' in chart_keys:
height = chart_settings['height']
if 'title' in chart_keys:
title = chart_settings['title']
if 'bar_width' in chart_keys:
bar_width = chart_settings['bar_width']
if 'lbl_fontsize' in chart_keys:
lbl_fontsize = chart_settings['lbl_fontsize']
if 'title_fontsize' in chart_keys:
title_fontsize = chart_settings['title_fontsize']
fig, ax = plt.subplots(figsize = (width, height))
ind = np.arange(len(df.index))
values = df[column]
rects = ax.bar(ind, values, bar_width, alpha=alpha)
ax.set_ylabel(ylabel, fontsize=lbl_fontsize)
ax.set_title(title + column, fontsize=title_fontsize)
ax.set_xticks(ind + 0.1 / 2)
ax.set_xticklabels(df.index)
plt.show()
uniq_interest_levels = list(train_data[target].unique())
interest_level_groups = train_data.groupby(target)
uniq_interest_levels
title = 'Number of occurrences in high interest level for '
title = 'Number of occurrences in medium interest level for '
plot_bar({'title': title, 'title_fontsize': 15}, get_value_counts('bathrooms', interest_level_groups.get_group('medium')), 'bathrooms') | code |
1005471/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
bathroom_df = train_data[['bathrooms', 'interest_level']]
def get_value_counts(col, df):
return pd.DataFrame(df[col].value_counts())
global_bar_chart_settings = {'height': 4, 'width': 8, 'bar_width': 0.9, 'title': 'Number of occurrences of ', 'ylabel': 'Occurrence', 'alpha': None, 'lbl_fontsize': 15, 'title_fontsize': 20}
def plot_bar(chart_settings, df, column):
width = global_bar_chart_settings['width']
height = global_bar_chart_settings['height']
alpha = global_bar_chart_settings['alpha']
title = global_bar_chart_settings['title']
bar_width = global_bar_chart_settings['bar_width']
ylabel = global_bar_chart_settings['ylabel']
lbl_fontsize = global_bar_chart_settings['lbl_fontsize']
title_fontsize = global_bar_chart_settings['title_fontsize']
chart_keys = chart_settings.keys()
if 'width' in chart_keys:
width = chart_settings['width']
if 'height' in chart_keys:
height = chart_settings['height']
if 'title' in chart_keys:
title = chart_settings['title']
if 'bar_width' in chart_keys:
bar_width = chart_settings['bar_width']
if 'lbl_fontsize' in chart_keys:
lbl_fontsize = chart_settings['lbl_fontsize']
if 'title_fontsize' in chart_keys:
title_fontsize = chart_settings['title_fontsize']
fig, ax = plt.subplots(figsize = (width, height))
ind = np.arange(len(df.index))
values = df[column]
rects = ax.bar(ind, values, bar_width, alpha=alpha)
ax.set_ylabel(ylabel, fontsize=lbl_fontsize)
ax.set_title(title + column, fontsize=title_fontsize)
ax.set_xticks(ind + 0.1 / 2)
ax.set_xticklabels(df.index)
plt.show()
plot_bar({}, get_value_counts('bathrooms', bathroom_df), 'bathrooms') | code |
1005471/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0] | code |
1005471/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
uniq_interest_levels = list(train_data[target].unique())
interest_level_groups = train_data.groupby(target)
uniq_interest_levels | code |
1005471/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005471/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
def get_value_counts(col, df):
return pd.DataFrame(df[col].value_counts())
global_bar_chart_settings = {'height': 4, 'width': 8, 'bar_width': 0.9, 'title': 'Number of occurrences of ', 'ylabel': 'Occurrence', 'alpha': None, 'lbl_fontsize': 15, 'title_fontsize': 20}
def plot_bar(chart_settings, df, column):
width = global_bar_chart_settings['width']
height = global_bar_chart_settings['height']
alpha = global_bar_chart_settings['alpha']
title = global_bar_chart_settings['title']
bar_width = global_bar_chart_settings['bar_width']
ylabel = global_bar_chart_settings['ylabel']
lbl_fontsize = global_bar_chart_settings['lbl_fontsize']
title_fontsize = global_bar_chart_settings['title_fontsize']
chart_keys = chart_settings.keys()
if 'width' in chart_keys:
width = chart_settings['width']
if 'height' in chart_keys:
height = chart_settings['height']
if 'title' in chart_keys:
title = chart_settings['title']
if 'bar_width' in chart_keys:
bar_width = chart_settings['bar_width']
if 'lbl_fontsize' in chart_keys:
lbl_fontsize = chart_settings['lbl_fontsize']
if 'title_fontsize' in chart_keys:
title_fontsize = chart_settings['title_fontsize']
fig, ax = plt.subplots(figsize = (width, height))
ind = np.arange(len(df.index))
values = df[column]
rects = ax.bar(ind, values, bar_width, alpha=alpha)
ax.set_ylabel(ylabel, fontsize=lbl_fontsize)
ax.set_title(title + column, fontsize=title_fontsize)
ax.set_xticks(ind + 0.1 / 2)
ax.set_xticklabels(df.index)
plt.show()
uniq_interest_levels = list(train_data[target].unique())
interest_level_groups = train_data.groupby(target)
uniq_interest_levels
title = 'Number of occurrences in high interest level for '
title = 'Number of occurrences in medium interest level for '
title = 'Number of occurrences in low interest level for '
plot_bar({'title': title, 'title_fontsize': 15}, get_value_counts('bathrooms', interest_level_groups.get_group('low')), 'bathrooms') | code |
1005471/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
def get_value_counts(col, df):
return pd.DataFrame(df[col].value_counts())
global_bar_chart_settings = {'height': 4, 'width': 8, 'bar_width': 0.9, 'title': 'Number of occurrences of ', 'ylabel': 'Occurrence', 'alpha': None, 'lbl_fontsize': 15, 'title_fontsize': 20}
def plot_bar(chart_settings, df, column):
width = global_bar_chart_settings['width']
height = global_bar_chart_settings['height']
alpha = global_bar_chart_settings['alpha']
title = global_bar_chart_settings['title']
bar_width = global_bar_chart_settings['bar_width']
ylabel = global_bar_chart_settings['ylabel']
lbl_fontsize = global_bar_chart_settings['lbl_fontsize']
title_fontsize = global_bar_chart_settings['title_fontsize']
chart_keys = chart_settings.keys()
if 'width' in chart_keys:
width = chart_settings['width']
if 'height' in chart_keys:
height = chart_settings['height']
if 'title' in chart_keys:
title = chart_settings['title']
if 'bar_width' in chart_keys:
bar_width = chart_settings['bar_width']
if 'lbl_fontsize' in chart_keys:
lbl_fontsize = chart_settings['lbl_fontsize']
if 'title_fontsize' in chart_keys:
title_fontsize = chart_settings['title_fontsize']
fig, ax = plt.subplots(figsize = (width, height))
ind = np.arange(len(df.index))
values = df[column]
rects = ax.bar(ind, values, bar_width, alpha=alpha)
ax.set_ylabel(ylabel, fontsize=lbl_fontsize)
ax.set_title(title + column, fontsize=title_fontsize)
ax.set_xticks(ind + 0.1 / 2)
ax.set_xticklabels(df.index)
plt.show()
uniq_interest_levels = list(train_data[target].unique())
interest_level_groups = train_data.groupby(target)
uniq_interest_levels
title = 'Number of occurrences in high interest level for '
plot_bar({'title': title, 'title_fontsize': 15}, get_value_counts('bathrooms', interest_level_groups.get_group('high')), 'bathrooms') | code |
1005471/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_json('../input/train.json')
display_count = 3
target = 'interest_level'
train_data.iloc[0]
bathroom_df = train_data[['bathrooms', 'interest_level']]
bathroom_df.head(display_count) | code |
32072743/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_test.loc[df_test['Province_State'].isnull(), 'Province_State'] = 'None'
df_test.isnull().sum() | code |
32072743/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.tail() | code |
32072743/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum() | code |
32072743/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range))
worst_affected = latest.sort_values(by='ConfirmedCases', ascending=False).head(20)
worst_affected.drop(columns=['Id', 'Date'], inplace=True)
worst_affected
worst_affected_locations = [worst_affected['Province_State'].iloc[i] if worst_affected['Province_State'].iloc[i] != 'None' else worst_affected['Country_Region'].iloc[i] for i in range(len(worst_affected))]
worst_affected_locations
plt.figure(figsize=(18, 9))
plt.bar(worst_affected_locations, worst_affected['ConfirmedCases'])
plt.title('Number of Confirmed Cases in the 20 Worst Affected Locations')
plt.ylabel('Number of confirmed cases')
plt.xticks(rotation='vertical')
plt.show() | code |
32072743/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range)) | code |
32072743/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range))
worst_affected = latest.sort_values(by='ConfirmedCases', ascending=False).head(20)
worst_affected.drop(columns=['Id', 'Date'], inplace=True)
worst_affected | code |
32072743/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
print('Number of unique province_country groups in test file: {}'.format(len(test_province_country_groups.groups.keys())))
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
print('Number of unique province_country groups in training file: {}'.format(len(province_country_groups.groups.keys()))) | code |
32072743/cell_45 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range))
worst_affected = latest.sort_values(by='ConfirmedCases', ascending=False).head(20)
worst_affected.drop(columns=['Id', 'Date'], inplace=True)
worst_affected
worst_affected_locations = [worst_affected['Province_State'].iloc[i] if worst_affected['Province_State'].iloc[i] != 'None' else worst_affected['Country_Region'].iloc[i] for i in range(len(worst_affected))]
worst_affected_locations
plt.xticks(rotation='vertical')
plt.figure(figsize=(18, 9))
plt.bar(worst_affected_locations, worst_affected['Fatalities'])
plt.title('Number of Fatalities in the 20 Worst Affected Locations')
plt.ylabel('Number of fatalities')
plt.xticks(rotation='vertical')
plt.show() | code |
32072743/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range))
province_country_groups = df_train.groupby(['Province_State', 'Country_Region']) | code |
32072743/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum() | code |
32072743/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_train.head() | code |
32072743/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
except IndexError:
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list
date_range = df_train['Date']
day_groups = df_train.groupby('Date')
latest = day_groups.get_group(max(date_range))
worst_affected = latest.sort_values(by='ConfirmedCases', ascending=False).head(20)
worst_affected.drop(columns=['Id', 'Date'], inplace=True)
worst_affected
worst_affected_locations = [worst_affected['Province_State'].iloc[i] if worst_affected['Province_State'].iloc[i] != 'None' else worst_affected['Country_Region'].iloc[i] for i in range(len(worst_affected))]
worst_affected_locations | code |
32072743/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
print('Number of unique province_country groups in training data: {}'.format(len(province_country_groups.groups.keys()))) | code |
32072743/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.info() | code |
32072743/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train['Date'] = pd.to_datetime(df_train['Date'], format='%Y-%m-%d')
df_test['Date'] = pd.to_datetime(df_test['Date'], format='%Y-%m-%d') | code |
32072743/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('prediction_challenge/covid19-global-forecasting-week-4/train.csv')
test_province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
df_train.isnull().sum()
df_train.loc[df_train['Province_State'].isnull(), 'Province_State'] = 'None'
df_train.isnull().sum()
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
province_country_groups = df_train.groupby(['Province_State', 'Country_Region'])
confirmed_cases_list = []
for p_c in province_country_groups.groups.keys():
confirmed_cases_list = province_country_groups.get_group(p_c)['ConfirmedCases'].tolist()
corrected = False
for i in range(len(confirmed_cases_list) - 1):
if confirmed_cases_list[i] > confirmed_cases_list[i + 1]:
try:
if confirmed_cases_list[i] <= confirmed_cases_list[i + 2]:
print('Correcting low data point. Replaced {0} with {1} for country/province {2}'.format(confirmed_cases_list[i + 1], confirmed_cases_list[i], p_c))
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
elif confirmed_cases_list[i - 1] <= confirmed_cases_list[i + 1]:
print('Correcting high data point. Replaced {0} with {1} for country/province {2}'.format(confirmed_cases_list[i], confirmed_cases_list[i - 1], p_c))
confirmed_cases_list[i] = confirmed_cases_list[i - 1]
else:
print('Not able to correct an erroneous point for for country/province {0} automatically'.format(p_c))
except IndexError:
print('Correcting penultimate data point. Replaced {0} with {1} for country/province {2}'.format(confirmed_cases_list[i + 1], confirmed_cases_list[i], p_c))
confirmed_cases_list[i + 1] = confirmed_cases_list[i]
corrected = True
if corrected == True:
print('Correcting for country/province {0}'.format(p_c))
df_train.loc[(df_train['Country_Region'] == p_c[1]) & (df_train['Province_State'] == p_c[0]), 'ConfirmedCases'] = confirmed_cases_list | code |
121148680/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filepath = '/kaggle/input/er-fast-track'
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv(f'{filepath}/heart.csv')
df.head(5) | code |
122264339/cell_4 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a | code |
122264339/cell_6 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
for i in b:
if i.isalpha() == True:
count = count + 1
if count == 26:
print('The string is a palangram ')
else:
print('The string is not a palangram') | code |
122264339/cell_2 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b | code |
122264339/cell_8 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
for i in b:
if i.isalpha() == True:
count = count + 1
A = (3, 4, 5, 6)
B = (4, 5)
count = 0
for i in B:
for j in A:
if i == j:
count = count + 1
break
if count == len(B):
print('B is a subset of A')
else:
print('B is not a subset of A') | code |
122264339/cell_3 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
print(count) | code |
122264339/cell_5 | [
"text_plain_output_1.png"
] | a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
b
a = 'The quick brown fox jumps over the lazy dog'
b = set(a)
count = 0
for i in b:
count = count + 1
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
a
a = 'The quick brown fox jumps over the lazy dog'
a = a.lower()
b = set(a)
b | code |
129039294/cell_21 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
df_ratings['year'] = df_ratings['date'].dt.year
df_ratings['year'].value_counts().plot()
plt.title('Number of ratings per year')
plt.show() | code |
129039294/cell_13 | [
"text_plain_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.info() | code |
129039294/cell_9 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum() | code |
129039294/cell_25 | [
"image_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
pd.set_option('display.max_rows', 100)
df_movies.groupby('year_of_release').count()
df_ratings['year'] = df_ratings['date'].dt.year
df_movie_rating_count = df_ratings.groupby('movie_id').count()
pd.DataFrame({'percentile': np.arange(5, 100, 5), 'n_ratings': np.percentile(df_movie_rating_count['rating'], np.arange(5, 100, 5)).astype('int')}) | code |
129039294/cell_4 | [
"image_output_1.png"
] | import glob
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files | code |
129039294/cell_20 | [
"text_html_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
pd.set_option('display.max_rows', 100)
df_movies.groupby('year_of_release').count() | code |
129039294/cell_19 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
df_movies.groupby('year_of_release')['movie_id'].count().plot()
plt.xticks(rotation=90)
plt.title('Number of movies by year')
plt.show() | code |
129039294/cell_7 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings.head() | code |
129039294/cell_18 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
df_movies.groupby('year_of_release')['movie_id'].count().plot()
plt.xticks(rotation=90)
plt.title('Number of movies by year')
plt.show() | code |
129039294/cell_28 | [
"image_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
pd.set_option('display.max_rows', 100)
df_movies.groupby('year_of_release').count()
df_ratings['year'] = df_ratings['date'].dt.year
df_movie_rating_count = df_ratings.groupby('movie_id').count()
pd.DataFrame({'percentile': np.arange(5, 100, 5), 'n_ratings': np.percentile(df_movie_rating_count['rating'], np.arange(5, 100, 5)).astype('int')})
today = '2005-01-01'
today_dt = pd.to_datetime(today)
df_ratings['days_from_rating'] = (today_dt - df_ratings['date']).dt.days.clip(lower=0)
df_ratings[['date', 'days_from_rating']].head() | code |
129039294/cell_8 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.info() | code |
129039294/cell_3 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129039294/cell_17 | [
"text_html_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
df_ratings['rating'].value_counts(normalize=True).plot(kind='bar')
plt.title('Proportions of ratings per rating values')
plt.show() | code |
129039294/cell_24 | [
"image_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
df_ratings['year'] = df_ratings['date'].dt.year
df_movie_rating_count = df_ratings.groupby('movie_id').count()
sns.ecdfplot(data=df_movie_rating_count, x='rating')
plt.title('CDF of number of ratings by movies')
plt.show() | code |
129039294/cell_14 | [
"text_html_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum() | code |
129039294/cell_22 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings['movie_id'] = np.where(df_ratings['rating'].isna(), df_ratings['customer_id'], np.nan)
df_ratings['movie_id'] = df_ratings['movie_id'].str.split(':').str[0]
df_ratings['movie_id'] = df_ratings['movie_id'].fillna(method='ffill')
df_ratings.dropna(subset=['rating', 'date'], inplace=True)
df_ratings = df_ratings.astype({'customer_id': 'int', 'movie_id': 'int'})
df_ratings.isna().sum()
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.isna().sum()
df_ratings.drop_duplicates(inplace=True)
df_movies.drop_duplicates(inplace=True)
plt.xticks(rotation=90)
plt.xticks(rotation=90)
df_ratings['year'] = df_ratings['date'].dt.year
df_movie_rating_count = df_ratings.groupby('movie_id').count()
sns.boxplot(data=df_movie_rating_count, x='rating')
plt.title('Number of ratings by movies')
plt.show() | code |
129039294/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_movies = pd.read_csv('/kaggle/working/modified_movie_titles.csv', header=None, names=['movie_id', 'year_of_release', 'title'], parse_dates=['year_of_release'], encoding='latin-1')
df_movies.head() | code |
129039294/cell_5 | [
"image_output_1.png"
] | import glob
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
rating_files = glob.glob('/kaggle/input/netflix-prize-data/combined_data_*.txt')
rating_files
df_ratings = pd.concat([pd.read_csv(filename, header=None, names=['customer_id', 'rating', 'date'], parse_dates=['date']) for filename in rating_files])
df_ratings.head() | code |
16123001/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
cat_col = Health_df.select_dtypes(exclude=np.number).drop(['Notes', 'Methods'], axis=1)
num_col = Health_df.select_dtypes(include=np.number)
cat_col = cat_col.apply(LabelEncoder().fit_transform)
final_df = pd.concat([cat_col, num_col], axis=1)
final_df.corr() | code |
16123001/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df['Indicator Category'].unique() | code |
16123001/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.head() | code |
16123001/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
cat_col = Health_df.select_dtypes(exclude=np.number).drop(['Notes', 'Methods'], axis=1)
num_col = Health_df.select_dtypes(include=np.number)
cat_col = cat_col.apply(LabelEncoder().fit_transform)
final_df = pd.concat([cat_col, num_col], axis=1)
final_df.head() | code |
16123001/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.describe() | code |
16123001/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error, r2_score
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
cat_col = Health_df.select_dtypes(exclude=np.number).drop(['Notes', 'Methods'], axis=1)
num_col = Health_df.select_dtypes(include=np.number)
lm = LinearRegression()
lm.fit(X_train, Y_train)
Y_train_predict = lm.predict(X_train)
Y_test_predict = lm.predict(X_test)
print('MSE Train:', mean_squared_error(Y_train, Y_train_predict))
print('MSE Test:', mean_squared_error(Y_test, Y_test_predict))
print('RMSE Train:', np.sqrt(mean_squared_error(Y_train, Y_train_predict)))
print('RMSE Test:', np.sqrt(mean_squared_error(Y_test, Y_test_predict)))
print('MAE Train', mean_absolute_error(Y_train, Y_train_predict))
print('MAE Test', mean_absolute_error(Y_test, Y_test_predict))
print('R2 Train', r2_score(Y_train, Y_train_predict))
print('R2 Test', r2_score(Y_test, Y_test_predict)) | code |
16123001/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train, Y_train) | code |
16123001/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
list(Health_df['Source'].unique()) | code |
16123001/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16123001/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique())) | code |
16123001/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum() | code |
16123001/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.info() | code |
16123001/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df[Health_df['Indicator Category'] == 'Demographics'] | code |
16123001/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df['Source'].value_counts() | code |
16123001/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train, Y_train)
print('Intercept value:', lm.intercept_)
print('Coefficient values:', lm.coef_) | code |
16123001/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
Health_df.apply(lambda x: len(x.unique()))
Health_df.isna().sum()
Health_df.dropna(subset=['Value'], inplace=True)
Health_df[Health_df['Value'] == 80977] | code |
16123001/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
list(Health_df['Indicator'].unique()) | code |
121149196/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121149196/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
if df.isnull().values.any():
print('There are empty cells in the dataframe')
else:
print('There are no empty cells in the dataframe') | code |
121149196/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
duplicates_P = df[df.duplicated(['PatientId'])]
if duplicates_P.empty:
print('There are no duplicates in the PatientId')
else:
print(f'There are {len(duplicates)} duplicates in the PatientId')
df.drop_duplicates(subset=['PatientId'], keep='first', inplace=True)
print(f'Removed {len(duplicates) - len(df)} duplicates')
duplicates_A = df.duplicated(subset='AppointmentID', keep='first')
if duplicates_A.sum() > 0:
df.drop_duplicates(subset='AppointmentID', keep='first', inplace=True)
print(f'{duplicates.sum()} duplicates found and removed.')
else:
print('There are no duplicates in the AppointmentID') | code |
17134452/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from plotly.tools import make_subplots
import advertools as adv
import pandas as pd
import plotly.graph_objs as go
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
adv.__version__
column_key = pd.read_csv('../input/column_key.csv')
column_key
clubs = pd.read_csv('../input/clubs.csv')
serp_clubs = pd.read_csv('../input/serp_clubs.csv')
serp_clubs['totalResults'] = serp_clubs['totalResults'].astype('int')
serp_clubs['queryTime'] = pd.to_datetime(serp_clubs['queryTime'])
serp_clubs.drop_duplicates(['searchTerms']).groupby('searchTerms', as_index=False).agg({'totalResults': 'sum'}).sort_values('totalResults', ascending=False).reset_index(drop=True).head(15).style.format({'totalResults': '{:,}'})
hl_domain_appearances = serp_clubs.groupby(['hl', 'displayLink']).agg({'rank': 'count'}).reset_index().sort_values(['hl', 'rank'], ascending=False).rename(columns={'rank': 'search_appearances'})
hl_domain_appearances.groupby(['hl']).head(5)
fig = make_subplots(1, 7, print_grid=False, shared_yaxes=True)
for i, lang in enumerate(serp_clubs['hl'].unique()[:7]):
df = serp_clubs[serp_clubs['hl'] == lang]
fig.append_trace(go.Bar(y=df['displayLink'].value_counts().values[:8], x=df['displayLink'].value_counts().index.str.replace('www.', '')[:8], name=lang, orientation='v'), row=1, col=i + 1)
fig.layout.margin = {'b': 150, 'r': 30}
fig.layout.legend.orientation = 'h'
fig.layout.legend.y = -0.5
fig.layout.legend.x = 0.15
fig.layout.title = 'Top Domains by Language of Search'
fig.layout.yaxis.title = 'Number of Appearances on SERPs'
fig.layout.plot_bgcolor = '#eeeeee'
fig.layout.paper_bgcolor = '#eeeeee'
iplot(fig) | code |
17134452/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
adv.__version__
column_key = pd.read_csv('../input/column_key.csv')
column_key
clubs = pd.read_csv('../input/clubs.csv')
serp_clubs = pd.read_csv('../input/serp_clubs.csv')
serp_clubs['totalResults'] = serp_clubs['totalResults'].astype('int')
serp_clubs['queryTime'] = pd.to_datetime(serp_clubs['queryTime'])
serp_clubs['displayLink'].value_counts()[:10] | code |
17134452/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lang_football = {'en': 'football', 'fr': 'football', 'de': 'fußball', 'es': 'fútbol', 'it': 'calcio', 'pt-BR': 'futebol', 'nl': 'voetbal'}
lang_football
len(lang_football) | code |
17134452/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
adv.__version__
column_key = pd.read_csv('../input/column_key.csv')
column_key
clubs = pd.read_csv('../input/clubs.csv')
clubs.head(10) | code |
17134452/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
adv.__version__
column_key = pd.read_csv('../input/column_key.csv')
column_key
clubs = pd.read_csv('../input/clubs.csv')
serp_clubs = pd.read_csv('../input/serp_clubs.csv')
serp_clubs['totalResults'] = serp_clubs['totalResults'].astype('int')
serp_clubs['queryTime'] = pd.to_datetime(serp_clubs['queryTime'])
serp_clubs.drop_duplicates(['searchTerms']).groupby('searchTerms', as_index=False).agg({'totalResults': 'sum'}).sort_values('totalResults', ascending=False).reset_index(drop=True).head(15).style.format({'totalResults': '{:,}'})
hl_domain_appearances = serp_clubs.groupby(['hl', 'displayLink']).agg({'rank': 'count'}).reset_index().sort_values(['hl', 'rank'], ascending=False).rename(columns={'rank': 'search_appearances'})
hl_domain_appearances.groupby(['hl']).head(5) | code |
17134452/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
adv.__version__
column_key = pd.read_csv('../input/column_key.csv')
column_key
clubs = pd.read_csv('../input/clubs.csv')
top_countries = clubs.groupby('Country').agg({'Total': 'sum'}).sort_values('Total', ascending=False).reset_index().head(10)
top_countries
clubs.groupby(['Country']).agg({'Club': 'count', 'Total': 'sum'}).sort_values('Club', ascending=False).reset_index().head(9).set_axis(['country', 'num_clubs', 'total_wins'], axis=1, inplace=False).assign(wins_per_club=lambda df: df['total_wins'].div(df['num_clubs'])).style.background_gradient(high=0.2) | code |
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