path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16169565/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from skimage import io, color, exposure, transform
import cv2
import cv2
import numpy as np # linear algebra
import os
import os
def preprocess_img(img):
# Histogram normalization in y
hsv = color.rgb2hsv(img)
hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2])
img = color.hsv2rgb(hsv)
# central ... | code |
17112602/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
T... | code |
17112602/cell_9 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
TRAIN_INPUT = 'twitgen_train_201906011956.csv'
VALID_INPUT = 'twitgen_valid_201906011956.csv'
TEST_INPUT = 'twitgen_test_201906011956.csv'
EMBEDDING_DIM = 512
MAXLEN = 50
basepath = '/kaggle/input/'
df_train = pd.read_csv(... | code |
17112602/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc
from sklearn.pipeline import Pipeline
from sklearn.prep... | code |
17112602/cell_6 | [
"image_output_1.png"
] | !ls $basepath | code |
17112602/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
17112602/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
TRAIN_INPUT = 'twitgen_train_201906011956.csv'
VALID_INPUT = 'twitgen_valid_201906011956.csv'
TEST_INPUT = 'twitgen_test_201906011956.csv'
EMBEDDING_DIM = 512
MAXLEN = 50
basepath = '/... | code |
17112602/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import ... | code |
17112602/cell_7 | [
"image_output_1.png"
] | import pandas as pd
TRAIN_INPUT = 'twitgen_train_201906011956.csv'
VALID_INPUT = 'twitgen_valid_201906011956.csv'
TEST_INPUT = 'twitgen_test_201906011956.csv'
EMBEDDING_DIM = 512
MAXLEN = 50
basepath = '/kaggle/input/'
df_train = pd.read_csv(basepath + TRAIN_INPUT, index_col=['id', 'time'], parse_dates=['time'])
df_... | code |
17112602/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import ... | code |
17112602/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import ... | code |
17112602/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
import pandas as pd
import ten... | code |
17112602/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import ... | code |
17112602/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
import pandas as pd
import ten... | code |
17112602/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
TRAIN_INPUT = 'twitgen_train_201906011956.csv'
VALID_INPUT = 'twitgen_valid_201906011956.csv'
TEST_INPUT = 'twitgen_test_201906011956.csv'
EMBEDDING_DIM = 512
MAXLEN = 50
basepath = '/kaggle/input/'
df_train = pd.read_csv(... | code |
17112602/cell_12 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
TRAIN_INPUT = 'twitgen_train_2019060119... | code |
90146477/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum() | code |
90146477/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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)
import seaborn as sns
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
... | code |
90146477/cell_23 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
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)
import seaborn as sns
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
... | code |
90146477/cell_44 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RepeatedStratifiedKFold,train_test_split,cross_val_score,RandomizedSearchCV,GridSearchCV
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | code |
90146477/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.head() | code |
90146477/cell_29 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/cell_41 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RepeatedStratifiedKFold,train_test_split,cross_val_score,RandomizedSearchCV,GridSearchCV
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | code |
90146477/cell_2 | [
"text_plain_output_1.png"
] | !pip install openpyxl --quiet | code |
90146477/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
df.info() | code |
90146477/cell_19 | [
"text_plain_output_1.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/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 |
90146477/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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)
import seaborn as sns
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
... | code |
90146477/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape | code |
90146477/cell_15 | [
"text_plain_output_1.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/cell_16 | [
"text_plain_output_1.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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)
import seaborn as sns
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
... | code |
90146477/cell_24 | [
"text_plain_output_1.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/cell_22 | [
"text_plain_output_1.png"
] | 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)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
numerical_cols = df.select_dtypes(include=np.number).co... | code |
90146477/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum() | code |
90146477/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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)
import seaborn as sns
df = pd.read_excel('/kaggle/input/pumpkin-seeds-dataset/Pumpkin_Seeds_Dataset.xlsx')
df.shape
df.duplicated().sum()
df.isna().sum()
... | code |
89132848/cell_9 | [
"text_plain_output_1.png"
] | import json
import torch
test_dir = '../input/birdclef-2022/test_soundscapes'
test_base_path = '../input/birdclef-2022/test.csv'
class_dict_base_path = '../input/birdclef-2022-saved-weights-and-misc/class_dict.json'
best_acc_mode_base_path = '../input/birdclef-2022-saved-weights-and-misc/birdclef2022-best_accuracy_mo... | code |
89132848/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
test_dir = '../input/birdclef-2022/test_soundscapes'
test_base_path = '../input/birdclef-2022/test.csv'
class_dict_base_path = '../input/birdclef-2022-saved-weights-and-misc/class_dict.json'
best_acc_mode_base_path = '../input/birdclef-2022-saved-weights-and-misc/birdclef2022-best_accuracy_model.pt... | code |
89132848/cell_6 | [
"text_plain_output_1.png"
] | import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
device | code |
89132848/cell_11 | [
"text_html_output_1.png"
] | from torchaudio.transforms import MelSpectrogram
from torchvision.transforms import Resize
augm = [MelSpectrogram(n_mels=128), Resize((128, 128))]
augm | code |
89132848/cell_5 | [
"text_plain_output_1.png"
] | import json
test_dir = '../input/birdclef-2022/test_soundscapes'
test_base_path = '../input/birdclef-2022/test.csv'
class_dict_base_path = '../input/birdclef-2022-saved-weights-and-misc/class_dict.json'
best_acc_mode_base_path = '../input/birdclef-2022-saved-weights-and-misc/birdclef2022-best_accuracy_model.pt'
best_l... | code |
105179048/cell_42 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import mplfinance as mpf
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BT... | code |
105179048/cell_13 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
ticker_df.tail() | code |
105179048/cell_9 | [
"image_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
tickers | code |
105179048/cell_30 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_20 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_29 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_26 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_41 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_19 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_18 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
depth = client.get_order_book(symbol='BTCUSDT')
depth | code |
105179048/cell_28 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_15 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
ticker_df.set_index('symbol', inplace=True)
float(ticker_df.loc['... | code |
105179048/cell_38 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_3 | [
"text_html_output_1.png"
] | !pip install python-binance pandas mplfinance | code |
105179048/cell_35 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_24 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
depth = client.get_order_book(symbol='BTCUSDT')
historical = client.get_historical_klines('ETHBTC', Client.KLINE_INTERVAL... | code |
105179048/cell_10 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
tickers[1]['price'] | code |
105179048/cell_37 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
105179048/cell_12 | [
"text_plain_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
ticker_df.head() | code |
105179048/cell_36 | [
"text_html_output_1.png"
] | from binance import Client, ThreadedWebsocketManager, ThreadedDepthCacheManager
import pandas as pd
apikey = 'YOURAPIKEY'
secret = 'YOURAPISECRET'
client = Client(apikey, secret)
tickers = client.get_all_tickers()
ticker_df = pd.DataFrame(tickers)
depth = client.get_order_book(symbol='BTCUSDT')
depth_df = pd.Dat... | code |
128027868/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_test.columns = ['label', 'title', 'text']
df_test.head() | code |
128027868/cell_30 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
import collections
import matplotlib.pyplot as plt
import pandas as pd
import re
import string
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.columns = ['label', 'tit... | code |
128027868/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.info() | code |
128027868/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Embedding, Dense, GlobalAveragePooling1D
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
import tensorflow as tf
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_te... | code |
128027868/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
from tensorflow.keras.layers import Embedding, Dense, GlobalAveragePooling1D
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import collections
import matplotlib.pyplot as plt
impor... | code |
128027868/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import string
import collections
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer, SnowballStemmer
import tensorflow as tf
from tensorflow.keras.layers import Embedding, Dense, Glob... | code |
128027868/cell_28 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
import collections
import matplotlib.pyplot as plt
import pandas as pd
import re
import string
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.columns = ['label', 'tit... | code |
128027868/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.columns = ['label', 'title', 'text']
df_train.head() | code |
128027868/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.columns = ['label', 'title', 'text']
df_test.columns = ['label', 'title', 'text']
df_train = df_train.head(100000)
df_test = df_test.head(10000)
def concat_co... | code |
128027868/cell_35 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Embedding, Dense, GlobalAveragePooling1D
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
import tensorflow as tf
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train... | code |
128027868/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.columns = ['label', 'title', 'text']
df_test.columns = ['label', 'title', 'text']
df_train = df_train.head(100000)
df_test = df_test.head(10000)
print(df_trai... | code |
128027868/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_test = pd.read_csv('/kaggle/input/amazon-reviews/test.csv')
df_train.head() | code |
128027868/cell_36 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Embedding, Dense, GlobalAveragePooling1D
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
import tensorflow as tf
df_train = pd.read_csv('/kaggle/input/amazon-reviews/train.csv')
df_te... | code |
327932/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.feature_selection import RFECV
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticReg... | code |
327932/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from patsy import dmatrices
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn import metrics
df = pd.read_csv('../input/train.csv') | code |
50228414/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from scipy.stats import mode
import matplotlib.pyplot as plt
import seaborn as sns
df... | code |
50228414/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from scipy.stats import mode
import matplotlib.pyplot as plt
import seaborn as sns
df... | code |
50228414/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from scipy.stats import mode
import matplotlib.pyplot as plt
import seaborn as sns
df_metabric = pd.read_csv('../input/breast-cancer-metabric... | code |
50228414/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from scipy.stats import mode
import matplotlib.pyplot as plt
import seaborn as sns
df... | code |
50228414/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from scipy.stats import mode
import matplotlib.pyplot as plt
import seaborn as sns
df_metabric = pd.read_csv('../input/breast-cancer-metabric... | code |
106196473/cell_4 | [
"text_plain_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/test_data.csv')
def agg_functions(df1)... | code |
106196473/cell_6 | [
"text_html_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd
import pycountry
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/test_data.csv')
def ... | code |
106196473/cell_2 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/test_data.csv')
display(df.isnull().sum())
def ag... | code |
106196473/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd
import pycountry
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/test_data.csv')
def ... | code |
106196473/cell_10 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd
import pycountry
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/test_data.csv')
def ... | code |
106196473/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd
import pycountry
import tensorflow as tf
import numpy as np
import pandas as pd
import gc
train = pd.read_csv('../input/digital-turbine-auction-bid-price-prediction/train_data.csv')
df = pd.read_csv('../input/digital-turbine-auction-bid-price-predic... | code |
1009481/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
import datetime
import numpy as np
import re
def calc_len_partial(X_train, limit=15):
i = 1
partial_len = len(X_train)
div_len = 0
while i:
if part... | code |
1009481/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import SGDClassifier
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.cross_validation import train_test_split
from nltk.corpus import stopwords
... | code |
1009481/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
import datetime
import numpy as np
import re
def calc_len_partial(X_train, limit=15):
i = 1
partial_len = len(X_train)
div_len = 0
while i:
if part... | code |
1009481/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_json('../input/train.json')
df[1:3] | code |
1009481/cell_12 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
import datetime
import numpy as np
import re
def calc_len_partial(X_train, limit=15):
i = 1
partial_len = len(X_train)
div_len = 0
while i:
if part... | code |
122250162/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
breast_cancer = load_breast_cancer(as_frame=True)
breast_cancer = load_breast_cancer()
breast_cancer.DESCR
breast_cancer.feature_names
breast_cancer.target_names
breast_cancer.data
breast_cancer.target | code |
122250162/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
breast_cancer = load_breast_cancer(as_frame=True)
breast_cancer = load_breast_cancer()
breast_cancer.DESCR | code |
122250162/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
breast_cancer = load_breast_cancer(as_frame=True)
breast_cancer = load_breast_cancer()
breast_cancer.DESCR
breast_cancer.feature_names
breast_cancer.target_names | code |
122250162/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
breast_cancer = load_breast_cancer(as_frame=True)
breast_cancer = load_breast_cancer()
breast_cancer.DESCR
breast_cancer.feature_names | code |
122250162/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
breast_cancer = load_breast_cancer(as_frame=True)
breast_cancer = load_breast_cancer()
breast_cancer.DESCR
breast_cancer.feature_names
breast_cancer.target_names
breast_cancer.data | code |
329301/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
329301/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.grid_search import GridSearchCV
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.label.values.ravel()
X_train = train.values[:, 1:]
X_test = test.va... | code |
329301/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA, RandomizedPCA
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = ... | code |
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