path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128020295/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 |
128020295/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
len(players) | code |
128020295/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
32068455/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from tqdm import tqdm
import glob
import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)
pd.options.mode.chained_assign... | code |
32068455/cell_4 | [
"text_html_output_1.png"
] | from pandarallel import pandarallel
import nltk
import spacy
import numpy as np
import pandas as pd
import glob
import json
import re
import itertools
from tqdm import tqdm
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
from nltk import tokenize
from sklearn.feature_extraction... | code |
32068455/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from langdetect import detect
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from os import path
from pandarallel import pandarallel
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from tqdm import tqdm
import contractio... | code |
32068455/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from langdetect import detect
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from os import path
from pandarallel import pandarallel
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from tqdm import tqdm
import contractio... | code |
32068455/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | # install packages
!pip install nltk --user
!pip install owlready2 --user
!pip install pronto --user
!pip install ipynb-py-convert --user
!pip install langdetect --user
!pip install contractions --user
!pip install inflect --user
!pip install num2words --user
!pip install tables --user
!pip install h5py --user
!pip ins... | code |
32068455/cell_11 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import glob
import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)
pd.options.mode.chained_assign... | code |
32068455/cell_16 | [
"text_plain_output_1.png"
] | from langdetect import detect
from tqdm import tqdm
import glob
import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)... | code |
32068455/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from langdetect import detect
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from os import path
from pandarallel import pandarallel
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from tqdm import tqdm
import contractio... | code |
32068455/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from langdetect import detect
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from os import path
from pandarallel import pandarallel
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from tqdm import tqdm
import contractio... | code |
32068455/cell_24 | [
"text_html_output_1.png"
] | from langdetect import detect
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from pandarallel import pandarallel
from tqdm import tqdm
import contractions
import glob
import json
import nltk
import pandas as pd # data processing, CSV file I/O (e.... | code |
32068455/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from langdetect import detect
from tqdm import tqdm
import glob
import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)... | code |
32068455/cell_10 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tqdm import tqdm
import glob
import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)
pd.options.mode.chained_assign... | code |
32068455/cell_27 | [
"text_plain_output_1.png"
] | tokenized_df['abstract_corpus'] = tokenized_df['abstract_token'].apply(lambda tokens: ' '.join(tokens))
corpus = tokenized_df['abstract_corpus'].tolist()
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
wordcloud = WordCloud(backgr... | code |
128029297/cell_20 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
from skimage.io import imread
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from tensorflow.keras.preprocessing import image
import cv2 as cv
import numpy as np
image_height = 128
image_width = 128
DATA = '... | code |
128029297/cell_7 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
from skimage.io import imread
from tensorflow.keras.preprocessing import image
import cv2 as cv
image_height = 128
image_width = 128
DATA = '/kaggle/input/malimg-dataset9010/dataset_9010/dataset_9010/malimg_dataset'
TRAIN_DATA = DATA + '/train'
VALIDATION_DATA = D... | code |
128029297/cell_18 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
from skimage.io import imread
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from tensorflow.keras.preprocessing import image
import cv2 as cv
import numpy as np
image_height = 128
image_width = 128
DATA = '/kagg... | code |
128029297/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from pathlib import Path
from skimage.io import imread
from sklearn.ensemble import RandomForestClassifier
from tensorflow.keras.preprocessing import image
import cv2 as cv
import numpy as np
image_height = 128
image_width = 128
DATA = '/kaggle/input/malimg-dataset9010/dataset_9010/datas... | code |
32070571/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum()
headline_length = all_sources['title'].str.len()
headline_length = all_sources['abstract'].str.len()
plt.hist(headline_l... | code |
32070571/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum() | code |
32070571/cell_44 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_55 | [
"text_html_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from nltk.stem import WordNetLemmatizer, PorterStemmer
from nltk.tokenize import word_tokenize
from sklearn.cluster import DBSCAN
from nltk.corpus import stopwords
from spacy.matcher import Matcher
from co... | code |
32070571/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from nltk.tokenize import word_tokenize
import gensim
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources = pd.... | code |
32070571/cell_39 | [
"text_plain_output_1.png"
] | import gc
del corpus, top_n_bigrams, lda_model, bow_corpus, top_tri_grams
gc.collect()
del embed_vectors, sentence_list, similarity_matrix
gc.collect() | code |
32070571/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_73 | [
"text_plain_output_1.png"
] | !pip install python-rake | code |
32070571/cell_41 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import spacy
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum()
nlp = spacy.load('en_core_web_sm')
sent_vecs = {}
docs = []
for i in tqdm(all_sources['title'].fillna('unknown')[:1000]):
doc = nlp(i)
... | code |
32070571/cell_54 | [
"text_html_output_1.png"
] | import gc
del corpus, top_n_bigrams, lda_model, bow_corpus, top_tri_grams
gc.collect()
del embed_vectors, sentence_list, similarity_matrix
gc.collect()
del biox, clean_comm
gc.collect() | code |
32070571/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum()
headline_length = all_sources['title'].str.len()
sns.distplot(headline_length)
plt.show() | code |
32070571/cell_60 | [
"text_html_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_19 | [
"image_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum(... | code |
32070571/cell_64 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import gc
del corpus, top_n_bigrams, lda_model, bow_corpus, top_tri_grams
gc.collect() | code |
32070571/cell_59 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_38 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
import tensorflow_hub as hub
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum()
def prepare_similarity(vectors):
similarity = cosine_similarity(vec... | code |
32070571/cell_75 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from nltk.tokenize import word_tokenize
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import RAKE
import matplotlib.pyplot as... | code |
32070571/cell_47 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import ... | code |
32070571/cell_17 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum(... | code |
32070571/cell_77 | [
"text_plain_output_1.png"
] | !pip install pytextrank | code |
32070571/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from nltk.tokenize import word_tokenize
import gensim
import matplotlib.pyplot as plt
import pandas as pd
import pyLDAvis
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
... | code |
32070571/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources ... | code |
32070571/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer,PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/CORD-19-research-challenge/'
all_sources ... | code |
32070571/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
import tensorflow_hub as hub
path = '../input/CORD-19-research-challenge/'
all_sources = pd.read_csv(path + 'metadata.csv')
all_sources.isna().sum()
def prepare_similarity(vectors):
similarity = cosine_similarity(vec... | code |
32070571/cell_5 | [
"text_plain_output_1.png"
] | !pip install rake-nltk | code |
74065110/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
weather_data = pd.read_csv('../input/buildingdatagenomeproject2/weather.csv', index_col='timestamp', parse_dates=True)
weather_data_site = weather_data[weather_data.si... | code |
74065110/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
elec_meter_data_all.info() | code |
74065110/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
weather_data = pd.read_csv('../input/buildingdatagenomeproject2/weather.csv', index_col='timestamp', parse_dates=True)
weather_data.info() | code |
74065110/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_15 | [
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
weather_data = pd.read_csv('../input/buildingdatagenomeproject2/weather.csv', index_col='timestamp', parse_dates=True)
weather_data_site = weather_data[weather_data.si... | code |
74065110/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
74065110/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
elec_meter_data_all = pd.read_csv('../input/buildingdatagenomeproject2/electricity.csv', index_col='timestamp', parse_dates=True)
site_data_example = elec_meter_data_all.loc[:, elec_meter_data_all.columns.str.contains('Panther') & elec_meter_data_all.columns.str.contains('office')]
site_data_examp... | code |
73078429/cell_13 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_9 | [
"image_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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_25 | [
"image_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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-... | code |
73078429/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-... | code |
73078429/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
sizes = [29941, 3967]
labels = ('NO', 'YES')
explode = (0, 0.1)
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, autopct='%1.1f%%', shadow=True, startangle=75)
ax1.axis('equal')
ax1.set_title('Client Churn Distribution')
ax1.legend(labels)
plt.show() | code |
73078429/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
import warnings
warnings.filterwarnings('ignore')
from time import time, strftime, gmtime
start = time()
import datetime
print(str(datetime.datet... | code |
73078429/cell_26 | [
"image_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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_11 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_19 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-... | code |
73078429/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-... | code |
73078429/cell_15 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_17 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Ch... | code |
73078429/cell_24 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-... | code |
73078429/cell_10 | [
"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/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Train.csv')
test_df = pd.read_csv('/kaggle/input/insurance-churn-prediction-weekend-hackathon/Insurance_Churn_ParticipantsData/Test.... | code |
73078429/cell_5 | [
"image_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 |
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