path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068402/cell_58 | [
"text_plain_output_1.png"
] | from datetime import datetime
from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
from typing import List
import contractions
import ftfy
import gensim.models.keyedvectors as word2vec
import numpy as np
import operator
import os
import pandas as pd
... | code |
32068402/cell_28 | [
"text_plain_output_1.png"
] | from gensim.models.phrases import Phraser
bigram_model = Phraser.load('../input/covid19phrasesmodels/covid_bigram_model_v0.pkl')
bigram_model['despite social media often vehicle fake news boast news hype also worth noting tremendous effort scientific community provide free uptodate information ongoing studies well cr... | code |
32068402/cell_8 | [
"text_html_output_10.png",
"text_html_output_22.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_23.png",
"text_html_output_19.png",
"... | ['2019-ncov', '2019 novel coronavirus', 'coronavirus 2019', 'coronavirus disease 19', 'covid-19', 'covid 19', 'ncov-2019', 'sars-cov-2', 'wuhan coronavirus', 'wuhan pneumonia', 'wuhan virus'] | code |
32068402/cell_80 | [
"text_html_output_1.png"
] | from IPython.display import display, HTML
from datetime import datetime
from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
from transformers import BartTokenizer, BartForConditionalGeneration
from typing import List
import contractions
import ftfy
im... | code |
32068402/cell_47 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vect... | code |
32068402/cell_46 | [
"image_output_1.png"
] | from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vect... | code |
32068402/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
print(f'Sentence count: {len(sentences_df)}') | code |
32068402/cell_14 | [
"text_plain_output_1.png"
] | !pip install contractions | code |
34128236/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z... | code |
34128236/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df['country'] = df['country'].str.strip()
display(df[['country']].describe())
display(df['country'].unique()) | code |
34128236/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['R_fighter', 'B_fighter']].describe() | code |
34128236/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['title_bout'].describe()) | code |
34128236/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['Winner'].describe())
print()
print(df['Winner'].unique()) | code |
34128236/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z... | code |
34128236/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['gender'].describe()) | code |
34128236/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
print(f'Number of fights including even fights and draws: {len(df)}')
print(f'Number of fights with ... | code |
34128236/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['location']].describe() | code |
34128236/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z... | code |
34128236/cell_49 | [
"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('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df['date'] = pd.to_datetime(df['date'])
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in yea... | code |
34128236/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['date']].describe() | code |
34128236/cell_38 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df['underdog'] = ''
red_underdog_mask = df['R_odds'] > df['B_odds']
blue_underdog_mask = df['B_odds'] > df['R_odds']
even_mask = df['B_odds'] == df['R_odds']
df['underdog'][red_underdog_mask] = 'Red... | code |
34128236/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['R_odds', 'B_odds']].describe() | code |
34128236/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z... | code |
34128236/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df['date'] = pd.to_datetime(df['date'])
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in yea... | code |
34128236/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df.info(verbose=True) | code |
34128236/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['weight_class'].describe())
print()
print(df['weight_class'].unique()) | code |
34128236/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df.info(verbose=True) | code |
130010382/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train ... | code |
130010382/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
data.h... | code |
130010382/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train ... | code |
130010382/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas_profiling
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Image
from sklearn.feature_selection import SelectKBest
fro... | code |
130010382/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train ... | code |
130010382/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
sample... | code |
49127503/cell_4 | [
"text_plain_output_1.png"
] | a, b, c = (10, 21, 0)
for i in range(10):
print(a)
c = a + b
a = b
b = c | code |
49127503/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | bil1 = int(input('Masukan Angka :'))
hasil = bil1 * bil1 * bil1
print('program konversi harga emas ke rupiah')
bil1 = int(input('masukan berat emas:'))
print('%d' % bil1)
hasil = bil1 * 10159000
print('harga emas %d gram adalah:Rp,%d' % (bil1, hasil)) | code |
49127503/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | bil1 = int(input('Masukan Angka :'))
hasil = bil1 * bil1 * bil1
print('Pangkat 3 dari bilangan %d adalah : %d' % (bil1, hasil)) | code |
49127503/cell_7 | [
"text_plain_output_1.png"
] | a, b, c = (10, 21, 0)
for i in range(10):
c = a + b
a = b
b = c
for a in range(50, 64, 4):
print(a, end=',')
for a in range(64, 74, 4):
print(a, end=',')
for a in range(74, 84, 4):
print(a, end=',')
for a in range(84, 94, 4):
print(a, end=',')
for a in range(94, 103, 4):
print(a, end=',... | code |
49127503/cell_5 | [
"text_plain_output_1.png"
] | for i in range(10, 0, -1):
print(' ' * (i - 1) + '*' * (11 - i) + '*' * (10 - i))
for i in range(10, 0, -1):
print(' ' * (10 - i) + '*' * i + '*' * (i - 1)) | code |
50231823/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target) | code |
50231823/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target) | code |
50231823/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.head() | code |
50231823/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape | code |
50231823/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
df['target'].value_counts() | code |
50231823/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.cp, df.target).plot(kind='bar', rot=0, xlabel='Chest Pain', ylabel='Frequency', title='Frequency Graph between the Chest Pain and Target') | code |
50231823/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.sex, df.target).plot(kind='bar', rot=0, ylabel='Frequency', xlabel='Sex', title='Frequency graph between the Sex and Target') | code |
50231823/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.describe() | code |
16150211/cell_42 | [
"text_html_output_1.png"
] | from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0']... | code |
16150211/cell_21 | [
"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/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f, ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=T... | code |
16150211/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=Tr... | code |
16150211/cell_44 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df... | code |
16150211/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.cor... | code |
16150211/cell_48 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.rea... | code |
16150211/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['origin'].value_counts().plot(kind='bar') | code |
16150211/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
k = df['train_type'].unique()
l = [x for x in range(len(k))]
df['train_type'].replace(k, l, inplace=True)
k = df['train_cl... | code |
16150211/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pickle
import datetime
import math
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Nadam
from keras.callbacks import ModelC... | code |
16150211/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
k = df['train_type'].unique()
l = [x for x in range(len(k))]
df['train_type'].replace(k, l, inplace=True)
df['train_class'... | code |
16150211/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df.head() | code |
16150211/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=Tr... | code |
16150211/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.head() | code |
16150211/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['train_type'].value_counts().plot(kind='bar')
k = df['train_type'].unique()
l = [x for x in range(len(k))]
print('Number... | code |
16150211/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=Tr... | code |
16150211/cell_46 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df... | code |
16150211/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['destination'].value_counts().plot(kind='bar') | code |
16150211/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum() | code |
90127400/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', axis=1)
df_test.head() | code |
90127400/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train.head() | code |
90127400/cell_6 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train.info() | code |
90127400/cell_2 | [
"text_html_output_1.png"
] | !nvidia-smi | code |
90127400/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90127400/cell_7 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
print(df_test) | code |
90127400/cell_8 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train.describe(include='all') | code |
90127400/cell_14 | [
"text_plain_output_1.png"
] | import cudf as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', a... | code |
90127400/cell_10 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_test.describe(include='all') | code |
90127400/cell_12 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', axis=1)
df_train.head() | code |
90127400/cell_5 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
print(df_train) | code |
1009478/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.describe() | code |
1009478/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.hist(train_data['Pclass'], color='l... | code |
1009478/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009478/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.head() | code |
128038775/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../inp... | code |
128038775/cell_26 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns... | code |
128038775/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../inp... | code |
128038775/cell_32 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns... | code |
128038775/cell_28 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns... | code |
128038775/cell_35 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
from tqdm import tqdm
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import time
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14}... | code |
128038775/cell_31 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns... | code |
128038775/cell_24 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns... | code |
128038775/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../inp... | code |
128038775/cell_37 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL.ImageDraw import Draw
from tqdm import tqdm
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import time
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14}... | code |
128038775/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../inp... | code |
2012216/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2012216/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if dat... | code |
2012216/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if dat... | code |
2012216/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2012216/cell_5 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png"... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if dat... | code |
89136278/cell_6 | [
"image_output_1.png"
] | !dir | code |
89136278/cell_18 | [
"text_plain_output_1.png"
] | import torch
import torchvision.models as models
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cnn = models.vgg19(pretrained=True).features.to(device).eval() | code |
89136278/cell_28 | [
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (51... | code |
89136278/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
import torch
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image... | code |
89136278/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import torch
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.To... | code |
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