path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89133561/cell_3 | [
"text_plain_output_1.png"
] | # Install pycocotools
!pip install pycocotools | code |
89133561/cell_12 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
from tqdm.notebook import tqdm
import json
import numpy as np
import os
import pandas as pd
thingClasses = ['Aortic enlargement', 'Atelectasis', 'Calcification', 'Cardiomegaly', 'Consolidation', 'ILD', 'Infiltration', 'Lung Opacity', 'Nodule/Mass', 'Other lesion', 'Pleural eff... | code |
105185383/cell_13 | [
"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 = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
... | code |
105185383/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes | code |
105185383/cell_20 | [
"image_output_1.png"
] | from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../i... | code |
105185383/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
print('Countries:', list... | code |
105185383/cell_19 | [
"text_plain_output_1.png"
] | """
def get_holidays(df):
years_list = [2017, 2018, 2019, 2020, 2021]
holiday_BE = holidays.CountryHoliday('BE', years = years_list)
holiday_FR = holidays.CountryHoliday('FR', years = years_list)
holiday_DE = holidays.CountryHoliday('DE', years = years_list)
holiday_IT = holidays.CountryHoliday('IT'... | code |
105185383/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 |
105185383/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
print('Train set shape:', train.shape)
print('Test set ... | code |
105185383/cell_15 | [
"image_output_1.png"
] | from scipy.stats import pearsonr
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 = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-20... | code |
105185383/cell_16 | [
"image_output_1.png"
] | from scipy.stats import pearsonr
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 = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-20... | code |
105185383/cell_14 | [
"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 = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
... | code |
105185383/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique() | code |
105185383/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
print('TRAIN:')
print('M... | code |
48165933/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn import metrics
from sklearn import metrics
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import ... | code |
48165933/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn import metrics
from sklearn import metrics
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import ... | code |
48165933/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import nltk
import re
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet') | code |
48165933/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn.datasets import fetch_20newsgroups
import re
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)
def clean... | code |
90153165/cell_13 | [
"text_plain_output_1.png"
] | from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax =... | code |
90153165/cell_9 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_770.png",
"text_plain_output_743.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_640.png",
"text_plain_output_822.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_... | from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax =... | code |
90153165/cell_4 | [
"text_plain_output_1.png"
] | from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.head() | code |
90153165/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.describe().transpose() | code |
90153165/cell_11 | [
"text_html_output_1.png"
] | from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax =... | code |
90153165/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum() | code |
90153165/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr() | code |
90153165/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import RobustScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
scaler = RobustScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
early_stop = EarlyS... | code |
90153165/cell_5 | [
"text_html_output_1.png"
] | from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.info() | code |
32070358/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
plt.imread('../input/ckplus/CK+48/fear/S091_001_00000013.png').shape | code |
32070358/cell_19 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image ... | code |
32070358/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
"\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n" | code |
32070358/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
im... | code |
32070358/cell_18 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
im... | code |
32070358/cell_15 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image ... | code |
32070358/cell_16 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image ... | code |
32070358/cell_3 | [
"text_plain_output_1.png"
] | import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
print(f'{dir1} has {count} images') | code |
32070358/cell_17 | [
"text_plain_output_1.png"
] | y_test[9:18] | code |
32070358/cell_14 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras import callbacks
filename = 'model_train_new.csv'
filepath = 'Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5'
csv_log = callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_bes... | code |
32070358/cell_12 | [
"text_plain_output_1.png"
] | from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import cv2
import numpy as np
import numpy as np # linear ... | code |
32070358/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
im... | code |
16118884/cell_63 | [
"text_html_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.dro... | code |
16118884/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.head(3) | code |
16118884/cell_56 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_65 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_54 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_67 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app... | code |
16118884/cell_60 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
app_data.head() | code |
16118884/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.dro... | code |
16118884/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.dro... | code |
16118884/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.dro... | code |
16118884/cell_58 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app... | code |
16118884/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
print('Missing Values' + '\n' + '-' * 15)
app_data.isnull().sum() | code |
16118884/cell_38 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app... | code |
16118884/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.dro... | code |
16118884/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data.head(4) | code |
16118884/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.appe... | code |
16118884/cell_36 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app... | code |
2036883/cell_13 | [
"text_html_output_1.png"
] | from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np... | code |
2036883/cell_9 | [
"text_html_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_4 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_11 | [
"text_html_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_1 | [
"image_output_5.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_7 | [
"text_html_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_8 | [
"text_html_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_15 | [
"text_plain_output_1.png"
] | from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
i... | code |
2036883/cell_3 | [
"text_html_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_14 | [
"text_plain_output_1.png"
] | from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data ... | code |
2036883/cell_10 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_12 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
2036883/cell_5 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.prepro... | code |
129018697/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T... | code |
129018697/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.head() | code |
129018697/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean() | code |
129018697/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import lightgbm as lgb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
im... | code |
129018697/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129018697/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T... | code |
129018697/cell_16 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/samp... | code |
129018697/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T... | code |
129018697/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T | code |
72081303/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd... | code |
72081303/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
f... | code |
72081303/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
df.head() | code |
72081303/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
t... | code |
72081303/cell_5 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
f... | code |
88087713/cell_42 | [
"text_plain_output_1.png"
] | se | code |
88087713/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fa... | code |
88087713/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kag... | code |
88087713/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_41 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kag... | code |
88087713/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88087713/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.