path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73071444/cell_36 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
... | code |
1008801/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data.query('trip_distance == 8000010.0')
jan_data = jan_data[jan_data.trip_distance != 8000010.0]
jan_data['trip_distance'].mean() | code |
1008801/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
max(jan_data['trip_distance']) | code |
1008801/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data.query('trip_distance == 8000010.0')
jan_data = jan_data[jan_data.trip_distance != 8000010.0]
jan_data = jan_data[jan_data.trip_distance < 13.4]
jan_data['trip_distance'].mean() | code |
1008801/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data.query('trip_distance == 8000010.0')
jan_data = jan_data[jan_data.trip_distance != 8000010.0]
jan_data = jan_data[jan_data.trip_distance < 13.4]
plt.hist(jan_data['trip_distance'], normed=True, bi... | code |
1008801/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data['trip_distance'][0:10] | code |
1008801/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data.query('trip_distance == 8000010.0')
jan_data = jan_data[jan_data.trip_distance != 8000010.0]
plt.hist(jan_data['trip_distance'], normed=True, bins=[1, 2, 3, 5, 50]) | code |
1008801/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
jan = '../input/1january.csv'
jan_data = pd.read_csv(jan)
jan_data.query('trip_distance == 8000010.0') | code |
122249728/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import os
import pandas as pd
import scipy.stats as stat
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
columns_to_drop = ['weight', 'payer_code', 'medical_... | code |
122249728/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import scipy.stats as stat
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
columns_to_drop = ['weight', 'payer_code', 'medical_specialty', 'max_glu_serum', 'A1Cresult']
df = df.drop(columns=columns_to_drop)
d... | code |
122249728/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import os
import pandas as pd
import scipy.stats as stat
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
columns_to_drop = ['weight', 'payer_code', 'medical_... | code |
122249728/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import os
import pandas as pd
import scipy.stats as stat
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
columns_to_drop = ['weight', 'payer_code', 'medical_... | code |
122249728/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
print('The shape of the dataset is {}.\n\n'.format(df.shape))
df.head() | code |
122249728/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
columns_to_drop = ['weight', 'payer_code', 'medical_specialty', 'max_glu_serum', 'A1Cresult']
df = df.drop(columns=columns_to_drop)
df = df.dropna()
missing_val... | code |
105191527/cell_42 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
india_nutrition_facts.info() | code |
105191527/cell_34 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_40 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_48 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_41 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_50 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_52 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_49 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutrition_facts.describe().style.apply(highlight_color, axis=0) | code |
105191527/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
def highlight_color(s):
return ['background-color: #FFC72C'] * 8 if s.name == 'Trans fat (g)' else [''] * 8
india_nutr... | code |
105191527/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
india_nutrition_facts = pd.read_csv('../input/mcdonalds-india-menu-nutrition-facts/India_Menu.csv')
india_nutrition_facts.head() | code |
16137293/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import gc
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
y = data.deal_probability.copy()
X_train, X_test, y_train, y_test = train_test_split(data, y, ... | code |
16137293/cell_18 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
import gc
import lightgbm as lgb
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sc... | code |
16137293/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
import gc
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
y = data.deal_probability.cop... | code |
16137293/cell_15 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
import gc
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/trai... | code |
16137293/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import gc
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
y = data.deal_probability.copy()
X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.2, random_state=23)
del data
gc.coll... | code |
1010626/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from glob import glob
basepath = '../input/train/'
all_cervix_images = []
for path in glob(basepath + '*'):
cervix_type = path.split('/')[-1]
cervix_images = glob(basepath + cervix_type + '/*')
all_c... | code |
1010626/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from skimage.io import imread, imshow
import cv2
from subprocess import check_output
print(check_output(['ls', '../input/train']).decode('utf8')) | code |
1010626/cell_5 | [
"text_html_output_1.png"
] | from glob import glob
import pandas as pd
from glob import glob
basepath = '../input/train/'
all_cervix_images = []
for path in glob(basepath + '*'):
cervix_type = path.split('/')[-1]
cervix_images = glob(basepath + cervix_type + '/*')
all_cervix_images = all_cervix_images + cervix_images
all_cervix_image... | code |
72075238/cell_25 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.is... | code |
72075238/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_dataset.tail() | code |
72075238/cell_23 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.is... | code |
72075238/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.isnull().sum() | code |
72075238/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_39 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.is... | code |
72075238/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset... | code |
72075238/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.head() | code |
72075238/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression(fit_intercept=False, normalize=False, n_jobs=-1)
model = clf.fit(X_train, y_train)
print(model.score(X_test, y_test)) | code |
72075238/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True... | code |
72075238/cell_27 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.is... | code |
72075238/cell_37 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression(fit_intercept=False, normalize=False, n_jobs=-1)
model = clf.fit(X_train, y_train)
result = model.predict(X_test)
result.shape | code |
72075238/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_dataset = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_dataset.fillna(0, inplace=True)
test_dataset.fillna(0, inplace=True)
train_dataset.is... | code |
88083684/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('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp['language'].nunique() | code |
88083684/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
df['id'] | code |
88083684/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df | code |
88083684/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp | code |
88083684/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 |
88083684/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('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp['language'] | code |
88083684/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns | code |
88083684/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('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp.columns
out = temp[temp['language'] == 'telugu']
out | code |
88083684/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp['language'].value_counts() | code |
88083684/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
temp = df.copy()
temp['language'] = [i.split('-')[0] for i in df['id']]
temp.columns | code |
88083684/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/tydiqa-bengali-telugu/tydiqa_secondary.csv')
df
df.columns
df['id'][100].split('-')[0] | code |
88081459/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import plot_... | code |
88081459/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
... | code |
88081459/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text impo... | code |
88081459/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
... | code |
88081459/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms.head() | code |
88081459/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classi... | code |
88081459/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop... | code |
88081459/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
... | code |
88081459/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
... | code |
88081459/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms.head() | code |
88081459/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['lab... | code |
88081459/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
88081459/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sms = pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
sms = sms.rename(columns={'v1': 'label', 'v2': 'text'})
sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
sms['label_num'] = sms[... | code |
105197650/cell_13 | [
"text_html_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df ... | code |
105197650/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
plt.figure(figsize=(18, 9))
plt... | code |
105197650/cell_2 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
data.head() | code |
105197650/cell_11 | [
"text_html_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df ... | code |
105197650/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
plt.xlabel = 'Dates'
plt.ylabel... | code |
105197650/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
from pandas.plotting import autocorrelation_plot
from pandas import DataFrame
from pandas import concat
import numpy as np
from math import sqrt
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from ... | code |
105197650/cell_18 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df ... | code |
105197650/cell_8 | [
"text_html_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df... | code |
105197650/cell_15 | [
"image_output_1.png"
] | from statsmodels.tools.eval_measures import rmse
from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-aus... | code |
105197650/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.head() | code |
105197650/cell_17 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production... | code |
105197650/cell_12 | [
"text_html_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df ... | code |
105197650/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/Electric_Production.csv')
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
df.head() | code |
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