path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34147773/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of... | code |
34147773/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34147773/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 20... | code |
34147773/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid') | code |
34147773/cell_40 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of... | code |
34147773/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
metadata_df | code |
34147773/cell_32 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34147773/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect th... | code |
34147773/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34147773/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
... | code |
34147773/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_df | code |
34147773/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import collections
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect... | code |
128023859/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
128023859/cell_9 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13... | code |
128023859/cell_4 | [
"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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns | code |
128023859/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape | code |
128023859/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
import numpy as np
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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = ... | code |
128023859/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
import os
for dirname, _, ... | code |
128023859/cell_8 | [
"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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
X.shape | code |
128023859/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = ... | code |
128023859/cell_12 | [
"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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
test_df | code |
128023859/cell_5 | [
"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('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
train_df | code |
50227784/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.head() | code |
50227784/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | plt.rcParams['figure.facecolor'] = 'white'
plt.rcParams['axes.facecolor'] = '#464646'
plt.rcParams['figure.figsize'] = (10, 7)
plt.rcParams['text.color'] = '#666666'
plt.rcParams['axes.labelcolor'] = '#666666'
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.color'] = '#666666... | code |
50227784/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
... | code |
50227784/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50227784/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
data_df['sentiment'].value_counts() | code |
50227784/cell_15 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.tokenize import word_tokenize
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
data_df = pd.read_csv('.... | code |
50227784/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
for i in range(10):
idx = np.random.randint(1, 50001)
data_df.he... | code |
50227784/cell_3 | [
"text_plain_output_1.png"
] | !pwd | code |
50227784/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
inp = Input(shape=(100,))
x = Embedding(20000, 256, trainable=False)(inp)
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25, recurrent_dropout=0.25))... | code |
50227784/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape | code |
50227784/cell_27 | [
"text_html_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
... | code |
50227784/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
for i in range(10):
idx = np.random.randint(1, 50001)
print('... | code |
128042900/cell_13 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentim... | code |
128042900/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix | code |
128042900/cell_11 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentim... | code |
128042900/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'")
data.query("asin == '0005092663'")
data.query("asin == ... | code |
128042900/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 |
128042900/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape | code |
128042900/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'")
data.query("asin == '0005092663'") | code |
128042900/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
X.head(20) | code |
128042900/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
new_pr.query("asin == '0310396336'") | code |
128042900/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'") | code |
128042900/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'") | code |
128042900/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentim... | code |
128042900/cell_10 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentimen... | code |
128042900/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
i = '04864... | code |
128042900/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
new_pr | code |
1007980/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
f... | code |
1007980/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
f... | code |
1007980/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from time import time
from sklearn.preprocessing import Normalizer
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
f... | code |
1007980/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import Normalizer
from time import time
import pandas as pd
start = time()
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
trainData = train_data.drop('label', 1)
trainLabel = train_data[['label']]
header = tr... | code |
105199186/cell_9 | [
"text_plain_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values()) | code |
105199186/cell_11 | [
"text_plain_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
noise_dist_normalized
sum(noise_dist_n... | code |
105199186/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
no... | code |
105199186/cell_10 | [
"image_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
noise_dist_normalized | code |
105199186/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import Image
Image('../input/noise-distpng/noise_dist.png') | code |
74058017/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
y_train = train['claim']
X_trai... | code |
74058017/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series... | code |
74052853/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
print(f'Number of rows in training dataset-----------... | code |
74052853/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.sha... | code |
74052853/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train.head() | code |
74052853/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim']) | code |
74052853/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 |
74052853/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr() | code |
74052853/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.sha... | code |
74052853/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.describe() | code |
74052853/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.sha... | code |
74052853/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
print(train.info())
print('=' * 50)
test.info() | code |
74052853/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
test.describe() | code |
74052853/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.sha... | code |
74052853/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
test.head() | code |
128004723/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_... | code |
128004723/cell_11 | [
"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 warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_... | code |
128004723/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 |
128004723/cell_10 | [
"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 warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_... | code |
128004723/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_... | code |
88085166/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['perce... | code |
88085166/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.describe() | code |
88085166/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.head() | code |
88085166/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['perce... | code |
88085166/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['perce... | code |
88085166/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.info() | code |
88085166/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, in... | code |
88085166/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['perce... | code |
88085166/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['perce... | code |
128023684/cell_21 | [
"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)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_23 | [
"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)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_first | code |
128023684/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_second.keys() | code |
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