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|---|---|---|---|
105193974/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 |
105193974/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
target_cols = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
train[target_cols].min()
train[target_cols].max() | code |
105193974/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(smooth_idf=True, sublinear_tf=True)
vectorizer.fit(raw_documents=train.full_text) | code |
105193974/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.head() | code |
50208360/cell_42 | [
"text_html_output_1.png"
] | from catboost import CatBoostRegressor, Pool
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
from catboost import CatBoostRegressor, Pool
from sklearn.metrics import r2_score, mean_squared_error
model = CatBoostRegressor(objective='RMSE')
model.fit(train[features], train[target]) | code |
50208360/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objs as go
cd = train['city_development_index'].value_counts().reset_index()
cd.columns = ['city_development_index', 'count']
cd['city_development_index'] = cd['city_development_index'].astype(str) + '-'
cd = cd.sort_values(['count']).tail(50)
fig = px.bar(cd, x='count', y='city_development_index', orientation='h', title='City development index', width=1000, height=900)
fig.show() | code |
50208360/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
mnj = train['target'].value_counts()
plt.figure(figsize=(6, 4))
sns.barplot(mnj.index, mnj.values, alpha=0.8)
plt.ylabel('Number of Data', fontsize=12)
plt.xlabel('target', fontsize=9)
plt.xticks(rotation=90)
plt.show() | code |
50208360/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
print('Any missing sample in training set:', train.isnull().values.any()) | code |
50208360/cell_33 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
train.info() | code |
50208360/cell_44 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor, Pool
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test = FunLabelEncoder(test)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
from catboost import CatBoostRegressor, Pool
from sklearn.metrics import r2_score, mean_squared_error
model = CatBoostRegressor(objective='RMSE')
model.fit(train[features], train[target])
predictions = model.predict(test[features])
predictions
from sklearn import metrics
fpr, tpr, thresholds = metrics.roc_curve(train[target], model.predict(train[features]))
metrics.auc(fpr, tpr) | code |
50208360/cell_20 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
def wmnj(x):
y = train[['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours', 'target']][train['education_level'] == x]
y = y.sort_values(by='enrollee_id', ascending=False)
return
wmnj('Graduate') | code |
50208360/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
train[target].head(100).values | code |
50208360/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
train[features].head(10) | code |
50208360/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
def wmnj(x):
y = train[['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours', 'target']][train['education_level'] == x]
y = y.sort_values(by='enrollee_id', ascending=False)
return
wmnj('Phd') | code |
50208360/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
mnj = train['target'].value_counts()
plt.xticks(rotation=90)
EL = train['education_level'].value_counts()
plt.figure(figsize=(6, 4))
sns.barplot(EL.index, EL.values, alpha=0.8)
plt.ylabel('Number of Data', fontsize=12)
plt.xlabel('education_level', fontsize=9)
plt.xticks(rotation=90)
plt.show() | code |
50208360/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pylab as pl
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.utils import shuffle
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import cross_val_score, GridSearchCV
import os
print(os.listdir('../input')) | code |
50208360/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
display(train[['city', 'city_development_index', 'relevent_experience', 'gender', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'target']].groupby(['gender', 'education_level', 'experience', 'company_size']).agg(['max', 'mean', 'min']).style.background_gradient(cmap='Oranges')) | code |
50208360/cell_45 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor, Pool
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test = FunLabelEncoder(test)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
from catboost import CatBoostRegressor, Pool
from sklearn.metrics import r2_score, mean_squared_error
model = CatBoostRegressor(objective='RMSE')
model.fit(train[features], train[target])
predictions = model.predict(test[features])
predictions
submission = pd.DataFrame({'enrollee_id': test['enrollee_id'], 'target': predictions})
submission.head(10) | code |
50208360/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
def wmnj(x):
y = train[['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours', 'target']][train['education_level'] == x]
y = y.sort_values(by='enrollee_id', ascending=False)
return
wmnj('Primary School') | code |
50208360/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objs as go
cd = train['city_development_index'].value_counts().reset_index()
cd.columns = [
'city_development_index',
'count'
]
cd['city_development_index'] = cd['city_development_index'].astype(str) + '-'
cd = cd.sort_values(['count']).tail(50)
fig = px.bar(
cd,
x='count',
y='city_development_index',
orientation='h',
title='City development index',
width=1000,
height=900
)
fig.show()
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objs as go
ep = train['experience'].value_counts().reset_index()
ep.columns = ['experience', 'percent']
ep['percent'] /= len(train)
fig = px.pie(ep, names='experience', values='percent', title='Experience', width=800, height=500)
fig.show() | code |
50208360/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train.head() | code |
50208360/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test.head() | code |
50208360/cell_43 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor, Pool
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test = FunLabelEncoder(test)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
from catboost import CatBoostRegressor, Pool
from sklearn.metrics import r2_score, mean_squared_error
model = CatBoostRegressor(objective='RMSE')
model.fit(train[features], train[target])
predictions = model.predict(test[features])
predictions | code |
50208360/cell_46 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor, Pool
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
train = train.replace([np.inf, -np.inf], np.nan)
train = train.fillna(0)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
train = FunLabelEncoder(train)
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test = FunLabelEncoder(test)
features = ['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours']
target = 'target'
from catboost import CatBoostRegressor, Pool
from sklearn.metrics import r2_score, mean_squared_error
model = CatBoostRegressor(objective='RMSE')
model.fit(train[features], train[target])
predictions = model.predict(test[features])
predictions
submission = pd.DataFrame({'enrollee_id': test['enrollee_id'], 'target': predictions})
filename = 'submission.csv'
submission.to_csv(filename, index=False)
print('Saved file: ' + filename) | code |
50208360/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
def wmnj(x):
y = train[['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours', 'target']][train['education_level'] == x]
y = y.sort_values(by='enrollee_id', ascending=False)
return
wmnj('High School') | code |
50208360/cell_22 | [
"text_html_output_2.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
def wmnj(x):
y = train[['enrollee_id', 'city', 'city_development_index', 'gender', 'relevent_experience', 'enrolled_university', 'education_level', 'major_discipline', 'experience', 'company_size', 'company_type', 'last_new_job', 'training_hours', 'target']][train['education_level'] == x]
y = y.sort_values(by='enrollee_id', ascending=False)
return
wmnj('Masters') | code |
50208360/cell_37 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
def FunLabelEncoder(df):
for c in df.columns:
if df.dtypes[c] == object:
le.fit(df[c].astype(str))
df[c] = le.transform(df[c].astype(str))
return df
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
test = FunLabelEncoder(test)
test.info() | code |
50208360/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
test = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_test.csv')
print('Any missing sample in test set:', test.isnull().values.any(), '\n') | code |
128049103/cell_9 | [
"image_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
min_year = life['Year'].min()
max_year = life['Year'].max()
print('Time frame of this data : {}-{}'.format(min_year, max_year))
region = life['Region'].unique()
print('Regions in this data : ', region)
print('Country in North America : ')
print(life[life['Region'] == 'North America']['Country'].unique())
print('Country in Asia : ')
print(life[life['Region'] == 'Asia']['Country'].unique())
print('Consist of ', len(life['Country'].unique())) | code |
128049103/cell_4 | [
"image_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
print(life.info()) | code |
128049103/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
plt.text(life[life['Year'] == 2000]['Life_expectancy'].median() + 2, y=0.015, s='Median life expectancy at year 2000', color='tan')
plt.text(life[life['Year'] == 2015]['Life_expectancy'].median() + 2, y=0.02, s='Median life expectancy at year 2015', color='darkcyan')
plt.xticks(rotation=90)
alc_mortal = life[['Year', 'Region', 'Country', 'Adult_mortality', 'Alcohol_consumption']]
sns.regplot(data=alc_mortal[alc_mortal['Region'] == 'European Union'], x='Alcohol_consumption', y='Adult_mortality', order=2, x_bins=20, color='darkcyan')
plt.title('Correlation between Alcohol consumption and Adult mortality in European population.')
plt.xlabel('Alcohol consumption(litre per capita)')
plt.ylabel('Adult mortality rate.')
plt.show() | code |
128049103/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import scipy.stats as stats
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
stat, pvalue = stats.wilcoxon(life[life['Year'] == 2015]['Life_expectancy'], life[life['Year'] == 2000]['Life_expectancy'])
print('There is significant difference between life expectancy of people around the world in year 2015 compared with 2000.')
print('Mean difference : ', life[life['Year'] == 2015]['Life_expectancy'].median() - life[life['Year'] == 2000]['Life_expectancy'].median(), 'years.')
print('P-value : ', pvalue) | code |
128049103/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life.head() | code |
128049103/cell_26 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
gdp_infant = life.groupby(['Country', 'Year'])[['GDP_per_capita', 'Infant_deaths', 'Under_five_deaths']].mean()
print('GDP per capita and infant & underfive year children mortality rates (per 1000) of countries around the world, since 2000-2015.')
gdp_infant = gdp_infant.reset_index()
print(gdp_infant)
gdp_infant_avg = life.groupby('Country')[['GDP_per_capita', 'Infant_deaths', 'Under_five_deaths']].mean()
print('Average GDP per capita and infant & underfive year children mortality rates (per 1000) of countries around the world, since 2000-2015.')
print(gdp_infant_avg) | code |
128049103/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score | code |
128049103/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
sns.kdeplot(data=life[life['Year'].isin([2000, 2015])], x='Life_expectancy', hue='Year', fill=True, palette=sns.color_palette('BrBG', 2))
plt.axvline(x=life[life['Year'] == 2000]['Life_expectancy'].median(), linestyle='--', color='tan')
plt.axvline(x=life[life['Year'] == 2015]['Life_expectancy'].median(), linestyle='--', color='darkcyan')
plt.text(life[life['Year'] == 2000]['Life_expectancy'].median() + 2, y=0.015, s='Median life expectancy at year 2000', color='tan')
plt.text(life[life['Year'] == 2015]['Life_expectancy'].median() + 2, y=0.02, s='Median life expectancy at year 2015', color='darkcyan')
plt.xlabel('Life expectancy')
plt.title('Average life expectancy of people around the world comparison between 2000 and 2015.')
plt.show()
sns.pointplot(data=life[life['Year'].isin([2000, 2015])], x='Region', y='Life_expectancy', hue='Year', estimator=np.median, palette=sns.color_palette('BrBG', 2))
plt.xlabel('Regions')
plt.ylabel('Life expectancy')
plt.xticks(rotation=90)
plt.title('Average life expectancy of population in each region comparison between 2000 and 2015')
plt.show() | code |
128049103/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
life.head() | code |
128049103/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
sns.lineplot(data=average_timeline)
plt.xlabel('Years')
plt.ylabel('Life expectancy')
plt.title('Average life expectancy around the world, since 2000-2015.')
plt.show()
sns.lineplot(data=life[life['Region'].isin(['Africa', 'Asia', 'North America'])], x='Year', y='Life_expectancy', hue='Region')
plt.xlabel('Years')
plt.ylabel('Life expectancy')
plt.title('Average life expectancy around the world, since 2000-2015.')
plt.show() | code |
128049103/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
print('Average life expectancy around the world, since 2000-2015.')
print(average_timeline)
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
print('Average life expectancy comparison between 2000 and 2015 of each area.')
print(average_compare) | code |
128049103/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
print('Average life expectancy around the world, since 2000-2015.')
print(average_timeline)
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
print('Average life expectancy comparison between 2000 and 2015 of each area.')
print(average_compare) | code |
128049103/cell_24 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import scipy.stats as stats
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
stat, pvalue = stats.wilcoxon(life[life['Year'] == 2015]['Life_expectancy'], life[life['Year'] == 2000]['Life_expectancy'])
alc_mortal = life[['Year', 'Region', 'Country', 'Adult_mortality', 'Alcohol_consumption']]
alc_mortal_EU = alc_mortal[alc_mortal['Region'] == 'European Union']
corr, pvalue = stats.spearmanr(alc_mortal_EU['Alcohol_consumption'], alc_mortal_EU['Adult_mortality'])
print('There is significant positive correlation between alcohol consumption and adult mortality rate in European population.')
print('Correlation coefficient : ', corr)
print('P-value : ', pvalue)
print('Caution for interpretion : It is important to note that correlation does not necessarily imply causation.') | code |
128049103/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
alc_mortal = life[['Year', 'Region', 'Country', 'Adult_mortality', 'Alcohol_consumption']]
print(alc_mortal)
print('Adult mortality rate : Probability of dying between 15 and 60 years.')
print('Alcohol consumption : Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol).') | code |
128049103/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
print(groupyears)
groupyears = groupyears.unstack()
print(groupyears)
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
print('Positions in dataframe where have missing country in each year : ', missing_country) | code |
128049103/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] = life[['Adult_mortality', 'Infant_deaths', 'Under_five_deaths']] / 10
groupyears = life.groupby('Year')['Country'].value_counts()
groupyears = groupyears.unstack()
missing_country = []
for index, rows in groupyears.iterrows():
for col in groupyears.columns:
if groupyears.loc[index, col] != 1:
missing_country.append([index, col])
else:
None
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
average_timeline = life.groupby('Year')['Life_expectancy'].mean()
average_compare = life[life['Year'].isin([2000, 2015])].groupby(['Year', 'Region', 'Country'])['Life_expectancy'].median()
gdp_infant = life.groupby(['Country', 'Year'])[['GDP_per_capita', 'Infant_deaths', 'Under_five_deaths']].mean()
gdp_infant = gdp_infant.reset_index()
gdp_infant_avg = life.groupby('Country')[['GDP_per_capita', 'Infant_deaths', 'Under_five_deaths']].mean()
usa_th_gdp = gdp_infant[gdp_infant['Country'].isin(['United States', 'Thailand'])]
print(usa_th_gdp) | code |
128049103/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
life = pd.read_csv('/kaggle/input/life-expectancy-who-updated/Life-Expectancy-Data-Updated.csv')
print(life.isna().sum()) | code |
72065286/cell_4 | [
"text_plain_output_1.png"
] | string = 'Hello World!'
print(string[0] + ' ' + string[6])
print(string[4] + ' ' + string[7])
print(string[2:4] + string[7:11])
print(string[::-1])
print(string[6:])
print(string[:5]) | code |
72065286/cell_6 | [
"text_plain_output_1.png"
] | str1 = 'Welcome2'
print('the alphabetic letter is:', str1.isalpha())
print('the lowercase letter is:', str1.islower())
print('the uppercase letter is:', str1.isupper())
print(str1, 'the alphanumeric is:', str1.isalnum())
str2 = 'Hello World!'
print('the alphabetic letter is:', str2.isalpha())
print('the lowercase letter is:', str2.islower())
print('the uppercase letter is:', str2.isupper())
str3 = 'Now is the best time ever!'
print('the alphabetic letter is:', str1.isalpha())
print('the lowercase letter is:', str1.islower())
print('the uppercase letter is:', str1.isupper())
print('the sentence starts with:', str3.startswith('Now'))
print('the sentence ends with:', str3.endswith('Now'))
str4 = '500017'
print('the alphanumeric is:', str4.isalnum())
print('the digits is:', str4.isdigit())
str5 = 'Iphone 6'
print('the alphanumeric is:', str5.isalnum())
print('the digits is:', str5.isdigit()) | code |
72065286/cell_2 | [
"text_plain_output_1.png"
] | print('hello world')
print('welcome to python language')
print('\nthis is a multi line string\n ') | code |
72065286/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | str1 = 'Welcome2'
str2 = 'Hello World!'
str3 = 'Now is the best time ever!'
str4 = '500017'
str5 = 'Iphone 6'
str1 = input('Enter the your own sentence:')
print('The input into title case:', str1.istitle()) | code |
49130814/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
df[(df['Age'] >= 40) & (df['Age'] <= 60)]['Age'].count() | code |
49130814/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
plt.figure(figsize=(14, 10))
sns.set_context('paper', font_scale=1.4)
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
49130814/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
plt.figure(figsize=(10, 8))
sns.set_context('paper', font_scale=1.5)
sns.histplot(x='Age', data=df, hue='satisfaction').set_title('Customer satisfaction by Age') | code |
49130814/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df.head() | code |
49130814/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
# Graphs of satisfaction customers by Class, Customer Type and Type of Travel.
sns.set_style('whitegrid')
fig, ax = plt.subplots(1,3, figsize=(18,16))
sns.set_context('paper', font_scale=1.5)
ax[0].set_title('Customer Satisfaction by Class')
sns.countplot(x='satisfaction', data = df, hue = 'Class', ax=ax[0])
ax[1].set_title('Customer Satisfaction by Customer Type')
sns.countplot(x='satisfaction', data = df, hue = 'Customer Type', ax=ax[1])
ax[2].set_title('Customer Satisfaction by Type of Travel')
sns.countplot(x='satisfaction', data = df, hue = 'Type of Travel', ax=ax[2])
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
crash_mx = df.corr()
# Creates a data frames that contains mean values. For satisfied and neutral or dissatisfied customers
# Eco and Eco Plus Classes
df_s = df[(df['satisfaction'] != 'neutral or dissatisfied') & (df['Class'] != 'Business')].describe()
df_nds = df[(df['satisfaction'] == 'neutral or dissatisfied') & (df['Class'] != 'Business')].describe()
# Creates a data frame that contains only a row with mean values for the selected featuers
# satisfied
df_s_mean = df_s[1:2][['Inflight wifi service', 'Departure/Arrival time convenient',
'Ease of Online booking', 'Gate location', 'Food and drink',
'Online boarding', 'Seat comfort', 'Inflight entertainment', 'On-board service',
'Leg room service', 'Baggage handling','Checkin service', 'Inflight service',
'Cleanliness']]
# Changing the name of index from 'mean' to 'satisfied'
df_s_mean = df_s_mean.rename(index = {'mean':'satisfied'})
###
# Creates a data frame that contains only a row with mean values for the selected featuers
# neutral or dissatisfied
df_nds_mean = df_nds[1:2][['Inflight wifi service', 'Departure/Arrival time convenient',
'Ease of Online booking', 'Gate location', 'Food and drink',
'Online boarding', 'Seat comfort', 'Inflight entertainment', 'On-board service',
'Leg room service', 'Baggage handling','Checkin service', 'Inflight service',
'Cleanliness']]
# Changing the name of index from 'mean' to 'neutral or dissatisfied'
df_nds_mean = df_nds_mean.rename(index = {'mean':'neutral or dissatisfied'})
###
# Combines two data frames into one
final_mean = pd.concat([df_nds_mean, df_s_mean])
final_mean
final_mean.T.plot(figsize=(16, 10), fontsize=15, kind='bar', title='Mean Grades in Eco and Eco Plus Class') | code |
49130814/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
# Graphs of satisfaction customers by Class, Customer Type and Type of Travel.
sns.set_style('whitegrid')
fig, ax = plt.subplots(1,3, figsize=(18,16))
sns.set_context('paper', font_scale=1.5)
ax[0].set_title('Customer Satisfaction by Class')
sns.countplot(x='satisfaction', data = df, hue = 'Class', ax=ax[0])
ax[1].set_title('Customer Satisfaction by Customer Type')
sns.countplot(x='satisfaction', data = df, hue = 'Customer Type', ax=ax[1])
ax[2].set_title('Customer Satisfaction by Type of Travel')
sns.countplot(x='satisfaction', data = df, hue = 'Type of Travel', ax=ax[2])
sns.set_style('whitegrid')
plt.figure(figsize=(25, 15))
sns.set_context('paper', font_scale=1.4)
crash_mx = df.corr()
sns.heatmap(crash_mx, annot=True, cmap='Blues') | code |
49130814/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
df['Arrival Delay in Minutes'].mean() | code |
49130814/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
plt.figure(figsize=(8, 6))
sns.set_context('paper', font_scale=1.5)
sns.countplot(x='satisfaction', data=df).set_title('Neutral or Dissatisfied vs Statisfied') | code |
49130814/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df.info() | code |
49130814/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
# Graphs of satisfaction customers by Class, Customer Type and Type of Travel.
sns.set_style('whitegrid')
fig, ax = plt.subplots(1,3, figsize=(18,16))
sns.set_context('paper', font_scale=1.5)
ax[0].set_title('Customer Satisfaction by Class')
sns.countplot(x='satisfaction', data = df, hue = 'Class', ax=ax[0])
ax[1].set_title('Customer Satisfaction by Customer Type')
sns.countplot(x='satisfaction', data = df, hue = 'Customer Type', ax=ax[1])
ax[2].set_title('Customer Satisfaction by Type of Travel')
sns.countplot(x='satisfaction', data = df, hue = 'Type of Travel', ax=ax[2])
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
crash_mx = df.corr()
df_s = df[(df['satisfaction'] != 'neutral or dissatisfied') & (df['Class'] != 'Business')].describe()
df_nds = df[(df['satisfaction'] == 'neutral or dissatisfied') & (df['Class'] != 'Business')].describe()
df_s_mean = df_s[1:2][['Inflight wifi service', 'Departure/Arrival time convenient', 'Ease of Online booking', 'Gate location', 'Food and drink', 'Online boarding', 'Seat comfort', 'Inflight entertainment', 'On-board service', 'Leg room service', 'Baggage handling', 'Checkin service', 'Inflight service', 'Cleanliness']]
df_s_mean = df_s_mean.rename(index={'mean': 'satisfied'})
df_nds_mean = df_nds[1:2][['Inflight wifi service', 'Departure/Arrival time convenient', 'Ease of Online booking', 'Gate location', 'Food and drink', 'Online boarding', 'Seat comfort', 'Inflight entertainment', 'On-board service', 'Leg room service', 'Baggage handling', 'Checkin service', 'Inflight service', 'Cleanliness']]
df_nds_mean = df_nds_mean.rename(index={'mean': 'neutral or dissatisfied'})
final_mean = pd.concat([df_nds_mean, df_s_mean])
final_mean | code |
49130814/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
df.info() | code |
49130814/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
plt.figure(figsize=(14, 10))
sns.set_context('paper', font_scale=1.4)
sns.heatmap(test.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
49130814/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(df['Arrival Delay in Minutes']).value_counts()
df['Arrival Delay in Minutes'] = df['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].mean())
np.isnan(df['Arrival Delay in Minutes']).value_counts()
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(test['Arrival Delay in Minutes']).value_counts() | code |
49130814/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(df['Arrival Delay in Minutes']).value_counts()
df['Arrival Delay in Minutes'] = df['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].mean())
np.isnan(df['Arrival Delay in Minutes']).value_counts()
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(test['Arrival Delay in Minutes']).value_counts()
test['Arrival Delay in Minutes'] = test['Arrival Delay in Minutes'].fillna(test['Arrival Delay in Minutes'].mean())
np.isnan(test['Arrival Delay in Minutes']).value_counts() | code |
49130814/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
fig, ax = plt.subplots(1, 3, figsize=(18, 16))
sns.set_context('paper', font_scale=1.5)
ax[0].set_title('Customer Satisfaction by Class')
sns.countplot(x='satisfaction', data=df, hue='Class', ax=ax[0])
ax[1].set_title('Customer Satisfaction by Customer Type')
sns.countplot(x='satisfaction', data=df, hue='Customer Type', ax=ax[1])
ax[2].set_title('Customer Satisfaction by Type of Travel')
sns.countplot(x='satisfaction', data=df, hue='Type of Travel', ax=ax[2]) | code |
49130814/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
test.info() | code |
49130814/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
df[df['Age'] < 40]['Age'].count() | code |
49130814/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(df['Arrival Delay in Minutes']).value_counts() | code |
49130814/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
test = pd.read_csv('../input/airline-passenger-satisfaction/test.csv')
test = test.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.5)
# Graphs of satisfaction customers by Class, Customer Type and Type of Travel.
sns.set_style('whitegrid')
fig, ax = plt.subplots(1,3, figsize=(18,16))
sns.set_context('paper', font_scale=1.5)
ax[0].set_title('Customer Satisfaction by Class')
sns.countplot(x='satisfaction', data = df, hue = 'Class', ax=ax[0])
ax[1].set_title('Customer Satisfaction by Customer Type')
sns.countplot(x='satisfaction', data = df, hue = 'Customer Type', ax=ax[1])
ax[2].set_title('Customer Satisfaction by Type of Travel')
sns.countplot(x='satisfaction', data = df, hue = 'Type of Travel', ax=ax[2])
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
crash_mx = df.corr()
df[df['Class'] != 'Business'].describe() | code |
49130814/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/train.csv')
df = df.drop(['Unnamed: 0', 'id'], axis=1)
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=1.4)
np.isnan(df['Arrival Delay in Minutes']).value_counts()
df['Arrival Delay in Minutes'] = df['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].mean())
np.isnan(df['Arrival Delay in Minutes']).value_counts() | code |
18105196/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow import keras
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255
x_val = x_val.reshape(-1, 28, 28, 1).astype('float32') / 255
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = keras.models.Sequential([keras.layers.Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1)), keras.layers.BatchNormalization(), keras.layers.Conv2D(32, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(32, kernel_size=5, strides=2, padding='same', activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Conv2D(64, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(64, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(64, kernel_size=5, strides=2, padding='same', activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Flatten(), keras.layers.Dense(128, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Dense(10, activation='softmax')])
model.summary()
datagen = keras.preprocessing.image.ImageDataGenerator(zoom_range=0.1, height_shift_range=0.1, width_shift_range=0.1, rotation_range=10)
datagen.fit(x_train)
model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.0001), metrics=['accuracy'])
batch_size = 32
epochs = 25
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), epochs=epochs, verbose=2, validation_data=(x_val, y_val), steps_per_epoch=x_train.shape[0] // batch_size) | code |
18105196/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow import keras
y_train = keras.utils.to_categorical(y_train)
y_val = keras.utils.to_categorical(y_val)
model = keras.models.Sequential([keras.layers.Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1)), keras.layers.BatchNormalization(), keras.layers.Conv2D(32, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(32, kernel_size=5, strides=2, padding='same', activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Conv2D(64, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(64, kernel_size=3, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Conv2D(64, kernel_size=5, strides=2, padding='same', activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Flatten(), keras.layers.Dense(128, activation='relu'), keras.layers.BatchNormalization(), keras.layers.Dropout(rate=0.4), keras.layers.Dense(10, activation='softmax')])
model.summary() | code |
106211640/cell_7 | [
"text_plain_output_1.png"
] | import os
ALPHABET_SIZE = 256
def badCharHeuristic(string, size):
badChar = [-1] * ALPHABET_SIZE
for i in range(size):
badChar[ord(string[i])] = i
return badChar
def BMMatch(text, pattern):
text = text.lower()
pattern = pattern.lower()
counter = 0
m = len(pattern)
n = len(text)
badChar = badCharHeuristic(pattern, m)
s = 0
while s <= n - m:
j = m - 1
while j >= 0 and pattern[j] == text[s + j]:
counter += 1
j -= 1
if j < 0:
return s
else:
counter += 1
s += max(1, j - badChar[ord(text[s + j])])
return counter
import os
os.listdir('/kaggle/input')
with open('../input/norskplaceholdertekst/Ibsen-PeerGynt2.txt', 'r') as file:
text = file.read()
patterns = ['prins', 'slutt', 'konge', 'lille', 'hjælp', 'kavri', 'serri', 'jenta', 'jente', 'stemm', 'elven', 'ørken', 'banan', 'bringe', 'vejen', 'vegen', 'veien', 'danse']
comparisons = []
for i in patterns:
counter = BMMatch(text, i)
comparisons.append(counter)
comp_per_char = []
for i in comparisons:
comp_per_char.append(i / len(text))
def average(list):
return sum(list) / len(list)
average(comp_per_char) | code |
106211640/cell_5 | [
"text_plain_output_1.png"
] | import os
ALPHABET_SIZE = 256
def badCharHeuristic(string, size):
badChar = [-1] * ALPHABET_SIZE
for i in range(size):
badChar[ord(string[i])] = i
return badChar
def BMMatch(text, pattern):
text = text.lower()
pattern = pattern.lower()
counter = 0
m = len(pattern)
n = len(text)
badChar = badCharHeuristic(pattern, m)
s = 0
while s <= n - m:
j = m - 1
while j >= 0 and pattern[j] == text[s + j]:
counter += 1
j -= 1
if j < 0:
return s
else:
counter += 1
s += max(1, j - badChar[ord(text[s + j])])
return counter
import os
os.listdir('/kaggle/input')
with open('../input/norskplaceholdertekst/Ibsen-PeerGynt2.txt', 'r') as file:
text = file.read()
patterns = ['prins', 'slutt', 'konge', 'lille', 'hjælp', 'kavri', 'serri', 'jenta', 'jente', 'stemm', 'elven', 'ørken', 'banan', 'bringe', 'vejen', 'vegen', 'veien', 'danse']
comparisons = []
for i in patterns:
counter = BMMatch(text, i)
comparisons.append(counter)
comp_per_char = []
for i in comparisons:
comp_per_char.append(i / len(text)) | code |
73081589/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.describe(include='all') | code |
73081589/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 |
73081589/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.head() | code |
73081589/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isna().sum() | code |
33106636/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
print('Train shape:', df_train.shape)
print('Test Shape:', df_test.shape) | code |
33106636/cell_20 | [
"image_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='darkgrid')
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
X_trainfull = df_train.drop(['SalePrice'], axis=1)
y = df_train.SalePrice
y = np.log1p(y)
d_temp = X_trainfull.isna().sum().sort_values(ascending=False)
d_temp = d_temp[d_temp > 0]
d_temp = d_temp / df_train.shape[0] * 100
plt.xlim(0, 100)
na_index = d_temp[d_temp > 20].index
X_trainfull.drop(na_index, axis=1, inplace=True)
num_cols = X_trainfull.corrwith(y).abs().sort_values(ascending=False).index
X_num = X_trainfull[num_cols]
X_cat = X_trainfull.drop(num_cols, axis=1)
X_num.sample(5) | code |
33106636/cell_8 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='darkgrid')
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
X_trainfull = df_train.drop(['SalePrice'], axis=1)
y = df_train.SalePrice
plt.figure(figsize=(8, 4))
plt.title('Distribution of Sales Price (y)')
sns.distplot(y)
plt.show() | code |
33106636/cell_24 | [
"image_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='darkgrid')
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
X_trainfull = df_train.drop(['SalePrice'], axis=1)
y = df_train.SalePrice
y = np.log1p(y)
d_temp = X_trainfull.isna().sum().sort_values(ascending=False)
d_temp = d_temp[d_temp > 0]
d_temp = d_temp / df_train.shape[0] * 100
plt.xlim(0, 100)
na_index = d_temp[d_temp > 20].index
X_trainfull.drop(na_index, axis=1, inplace=True)
num_cols = X_trainfull.corrwith(y).abs().sort_values(ascending=False).index
X_num = X_trainfull[num_cols]
X_cat = X_trainfull.drop(num_cols, axis=1)
X_num.sample(5)
high_corr_num = X_num.corrwith(y)[X_num.corrwith(y).abs() > 0.5].index
X_num = X_num[high_corr_num]
plt.figure(figsize=(10, 6))
sns.heatmap(X_num.corr(), annot=True, cmap='coolwarm')
plt.show()
print('Correlation of Each feature with target')
X_num.corrwith(y) | code |
33106636/cell_10 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='darkgrid')
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
X_trainfull = df_train.drop(['SalePrice'], axis=1)
y = df_train.SalePrice
y = np.log1p(y)
plt.figure(figsize=(8, 4))
plt.title('Distribution of log Sales Price (y)')
sns.distplot(y)
plt.xlabel('Log of Sales Price')
plt.show() | code |
33106636/cell_12 | [
"image_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='darkgrid')
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from warnings import filterwarnings
filterwarnings('ignore')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
X_trainfull = df_train.drop(['SalePrice'], axis=1)
y = df_train.SalePrice
y = np.log1p(y)
d_temp = X_trainfull.isna().sum().sort_values(ascending=False)
d_temp = d_temp[d_temp > 0]
d_temp = d_temp / df_train.shape[0] * 100
plt.figure(figsize=(8, 5))
plt.title('Features Vs Percentage Of Null Values')
sns.barplot(y=d_temp.index, x=d_temp, orient='h')
plt.xlim(0, 100)
plt.xlabel('Null Values (%)')
plt.show() | code |
89132547/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
df.describe() | code |
89132547/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum() | code |
89132547/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
customertype_x_city = df.groupby('City')['Customer type'].value_counts()
customertype_x_city
#visualize Customer type per City
best_payment_x_city_bar = px.histogram(df, x='Customer type', color='City')
best_payment_x_city_bar.show()
best_payment_x_city = df.groupby('City')['Payment'].value_counts()
best_payment_x_city
#visualize payment per City
best_payment_x_city_bar = px.histogram(df, x='Payment', color='City')
best_payment_x_city_bar.show()
total_per_date = px.bar(df, x='Total', y='City', color='City', animation_frame='Date', animation_group='City')
total_per_date.show() | code |
89132547/cell_6 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.head() | code |
89132547/cell_2 | [
"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/supermarket-sales'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89132547/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
customertype_x_city = df.groupby('City')['Customer type'].value_counts()
customertype_x_city
#visualize Customer type per City
best_payment_x_city_bar = px.histogram(df, x='Customer type', color='City')
best_payment_x_city_bar.show()
best_payment_x_city = df.groupby('City')['Payment'].value_counts()
best_payment_x_city
best_payment_x_city_bar = px.histogram(df, x='Payment', color='City')
best_payment_x_city_bar.show() | code |
89132547/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.info() | code |
89132547/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
customertype_x_city = df.groupby('City')['Customer type'].value_counts()
customertype_x_city
best_payment_x_city = df.groupby('City')['Payment'].value_counts()
best_payment_x_city | code |
89132547/cell_8 | [
"text_html_output_1.png"
] | !mitosheet | code |
89132547/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
df['Customer type'].value_counts() | code |
89132547/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
customertype_x_city = df.groupby('City')['Customer type'].value_counts()
customertype_x_city | code |
89132547/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
customertype_x_city = df.groupby('City')['Customer type'].value_counts()
customertype_x_city
best_payment_x_city_bar = px.histogram(df, x='Customer type', color='City')
best_payment_x_city_bar.show() | code |
89132547/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
df['Gender'].value_counts() | code |
89132547/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.isnull().sum()
df['Invoice ID'].duplicated().sum() | code |
89125628/cell_56 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
scaler = StandardScaler()
df = scaler.fit_transform(df)
df = pd.DataFrame(df)
plt = df.plot.bar()
df = pd.DataFrame([[180000, 110, 18.9, 1400], [360000, 905, 23.4, 1800], [230000, 230, 14.0, 1300], [60000, 450, 13.5, 1500]], columns=['Col A', 'Col B', 'Col C', 'Col D'])
import matplotlib.pyplot as plt
plt = df.plot.bar() | code |
89125628/cell_54 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
scaler = StandardScaler()
df = scaler.fit_transform(df)
df = pd.DataFrame(df)
plt = df.plot.bar()
df = pd.DataFrame([[180000, 110, 18.9, 1400], [360000, 905, 23.4, 1800], [230000, 230, 14.0, 1300], [60000, 450, 13.5, 1500]], columns=['Col A', 'Col B', 'Col C', 'Col D'])
display(df) | code |
89125628/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
from sklearn.preprocessing import StandardScaler
details = {'col1': [1, 3, 5, 7, 9], 'col2': [7, 4, 35, 14, 56]}
df = pd.DataFrame(details)
print(df)
scaler = StandardScaler()
df = scaler.fit_transform(df)
df = pd.DataFrame(df)
print(df) | code |
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