path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
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-lear... | 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... | 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-lear... | 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.preproce... | 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 = ... | 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)
p... | 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()
... | 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.fil... | 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', 'compan... | 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()
... | 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()
... | 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', 'compan... | 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.inde... | 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
pr... | 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_l... | 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.preproce... | 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', 'compan... | 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... | 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.preproce... | 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.preproce... | 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', 'compan... | 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', 'compan... | 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:
... | 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... | 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_death... | 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')['C... | 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()
group... | 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... | 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_death... | 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
groupyear... | 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()
group... | 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()
group... | 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')['C... | 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()
group... | 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... | 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()
group... | 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 lette... | 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(), ytickl... | 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... | 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... | 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... | 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... | 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... | 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... | 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']... | 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']... | 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... | 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']... | 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... | 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']... | 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, ac... | 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... | 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... | 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... | 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 sk... | 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... | 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 sk... | 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 sk... | 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 sk... | 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 pe... | 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 pe... | 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'].valu... | 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 = p... | 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_t... | 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... | 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.