path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88100444/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.head() | code |
88100444/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 |
88100444/cell_7 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV f... | code |
88100444/cell_8 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV f... | code |
88100444/cell_3 | [
"image_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.r... | code |
88100444/cell_5 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
... | code |
1005328/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([r... | code |
1005328/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([r... | code |
1005328/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
new_price_df = price_df[price_df['pred_price_ratio'] < 4]
plt.figure(figsize=(10, 20))
sns.boxplot(x='interest_level', y='pred_price_ratio', data=new_price_df)
plt.title('Interest Level and Price / Predicted Price Ratio', fontsize=32)
plt.show() | code |
1005328/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([r... | code |
33119806/cell_21 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all ... | code |
33119806/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
d... | code |
33119806/cell_34 | [
"text_plain_output_1.png"
] | model_outputs | code |
33119806/cell_30 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_lab... | code |
33119806/cell_33 | [
"text_plain_output_4.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | result | code |
33119806/cell_44 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-... | code |
33119806/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['s... | code |
33119806/cell_39 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['s... | code |
33119806/cell_26 | [
"text_plain_output_1.png"
] | !pip install simpletransformers | code |
33119806/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
d... | code |
33119806/cell_19 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , ... | code |
33119806/cell_45 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-... | code |
33119806/cell_18 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , ... | code |
33119806/cell_28 | [
"text_plain_output_5.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
print(train.shape) | code |
33119806/cell_8 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df.head() | code |
33119806/cell_16 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , ... | code |
33119806/cell_38 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , t... | code |
33119806/cell_17 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , ... | code |
33119806/cell_43 | [
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-... | code |
33119806/cell_31 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_lab... | code |
33119806/cell_46 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-... | code |
33119806/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
d... | code |
33119806/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all ... | code |
33119806/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
d... | code |
33119806/cell_27 | [
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False) | code |
33119806/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['s... | code |
33119806/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
d... | code |
90144662/cell_13 | [
"text_plain_output_1.png"
] | from pdf2image import convert_from_path,convert_from_bytes
import easyocr
PDF_file = '../input/osbook/operating-system-concepts-9th-edition.pdf'
'\nPart #1 : Converting PDF to images\n'
pages = convert_from_path(PDF_file, dpi=100, thread_count=4)
type(pages[0])
image_counter = 1
for page in pages:
filename = 'pa... | code |
90144662/cell_4 | [
"text_plain_output_1.png"
] | !apt-get install poppler-utils -y | code |
90144662/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 |
90144662/cell_14 | [
"text_plain_output_1.png"
] | from pdf2image import convert_from_path,convert_from_bytes
import easyocr
PDF_file = '../input/osbook/operating-system-concepts-9th-edition.pdf'
'\nPart #1 : Converting PDF to images\n'
pages = convert_from_path(PDF_file, dpi=100, thread_count=4)
type(pages[0])
image_counter = 1
for page in pages:
filename = 'pa... | code |
16164242/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import KFold
import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
de... | code |
16164242/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_... | code |
16164242/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
import os
print(os.listdir('../input')) | code |
16164242/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_... | code |
16164242/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
... | code |
16164242/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def... | code |
18154734/cell_9 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
18154734/cell_6 | [
"text_html_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
18154734/cell_2 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
print(len(LANG_FILENAMES))
LANG_FILENAMES[0... | code |
18154734/cell_11 | [
"text_html_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
18154734/cell_8 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
18154734/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
18154734/cell_12 | [
"text_plain_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBu... | code |
33105253/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.info() | code |
33105253/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.head() | code |
33105253/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/... | code |
33105253/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.describe() | code |
33105253/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train = pd.read_c... | code |
33105253/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train = pd.read_c... | code |
33105253/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns | code |
34121307/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', '... | code |
34121307/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.head() | code |
34121307/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
test.info() | code |
34121307/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 |
34121307/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', 'Address'], inplace=True, axis=1)
test.drop(['Id'... | code |
34121307/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
print(train['Resolution'].unique())
len(train['Resolution'].unique()) | code |
34121307/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
test.head() | code |
34121307/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', 'Address'], inplace=True, axis=1)
test.drop(['Id'... | code |
34121307/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum() | code |
1007790/cell_2 | [
"image_output_1.png"
] | from sklearn import cluster, datasets
import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.r... | code |
1007790/cell_1 | [
"text_plain_output_1.png"
] | from sklearn import cluster, datasets
import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.r... | code |
1007790/cell_3 | [
"image_output_1.png"
] | from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.nei... | code |
89127043/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv('../input/santander-customer-satisfaction/train.csv')
test_df = pd.read_csv('../input/santander-customer-sa... | code |
89127043/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv('../input/santander-customer-satisfaction/train.csv')
test_df = pd.read_csv('../input/santander-customer-satisfaction/test.csv'... | code |
320432/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
full.Embarked.value_counts().plot(kind='bar') | code |
320432/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tit... | code |
320432/cell_20 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked... | code |
320432/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
titanic.info()
print('-' * 40)
test.info() | code |
320432/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tes... | code |
320432/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tit... | code |
320432/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
ful... | code |
320432/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts() | code |
320432/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
ful... | code |
320432/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tit... | code |
320432/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tes... | code |
320432/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
ful... | code |
320432/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
tit... | code |
320432/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
test[np.isnan(test['Fare'])] | code |
32062669/cell_21 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train... | code |
32062669/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.appl... | code |
32062669/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
train_df.head(5) | code |
32062669/cell_23 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/cell_30 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.f... | code |
32062669/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.rea... | code |
32062669/cell_29 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_26 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.f... | code |
32062669/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import torch
gpu_status = torch.cuda.is_available()
if not gpu_status:
print('No GPU, Using CPU')
else:
print('Using GPU') | code |
32062669/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_... | code |
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