path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106196484/cell_16 | [
"text_plain_output_1.png"
] | from flashtext import KeywordProcessor
from nltk.tokenize import sent_tokenize
import pke
import re
import re
ml_ftxt = re.sub('\\n', ' ', ml_ftxt)
ml_ftxt = ml_ftxt.translate(str.maketrans(' ', ' ', '!"#$%&\'()*+-/:;<=>?@[\\]^_`{|}~'))
ml_ftxt = re.sub('[A-Za-z0-9]*@[A-Za-z]*\\.?[A-Za-z0-9]*', '', ml_ftxt)
extrac... | code |
106196484/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | ml_ftxt[:3000] | code |
106196484/cell_14 | [
"text_plain_output_1.png"
] | !pip install --upgrade pip
!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab] | code |
106196484/cell_10 | [
"text_plain_output_1.png"
] | import pke
import re
import re
ml_ftxt = re.sub('\\n', ' ', ml_ftxt)
ml_ftxt = ml_ftxt.translate(str.maketrans(' ', ' ', '!"#$%&\'()*+-/:;<=>?@[\\]^_`{|}~'))
ml_ftxt = re.sub('[A-Za-z0-9]*@[A-Za-z]*\\.?[A-Za-z0-9]*', '', ml_ftxt)
extractor = pke.unsupervised.TextRank()
extractor.load_document(input=ml_ftxt)
extracto... | code |
106196484/cell_12 | [
"text_plain_output_1.png"
] | !pip install -U transformers==3.0.0
!python -m nltk.downloader punkt
!git clone https://github.com/patil-suraj/question_generation.git | code |
106196484/cell_5 | [
"text_plain_output_1.png"
] | import re
import re
ml_ftxt = re.sub('\\n', ' ', ml_ftxt)
ml_ftxt = ml_ftxt.translate(str.maketrans(' ', ' ', '!"#$%&\'()*+-/:;<=>?@[\\]^_`{|}~'))
ml_ftxt = re.sub('[A-Za-z0-9]*@[A-Za-z]*\\.?[A-Za-z0-9]*', '', ml_ftxt)
ml_ftxt[:3000] | code |
88098897/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('iris.csv')
df | code |
129020311/cell_25 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
print(train.shape)
print(test.shape) | code |
129020311/cell_33 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_20 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('PassengerId', inplace=True)
data.head() | code |
129020311/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_26 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_11 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129020311/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)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('PassengerId', inplace=True)
data.drop('... | code |
129020311/cell_18 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=F... | code |
129020311/cell_32 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_15 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_16 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_17 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.sha... | code |
129020311/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_22 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_12 | [
"text_plain_output_1.png"
] | 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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape
data.set_index('Pas... | code |
129020311/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
data = pd.concat([train, test], axis=0, sort=False, ignore_index=True)
data.shape | code |
1004133/cell_4 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_28.png",
"text_plain_output_20.png",
"image_output_23.png",
"text_plain_output_4.pn... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.preprocessing as pre
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
images = train_data.drop('label', 1)
observations, features = images.shape
pixel_width = int(np.sqrt(features))
X = i... | code |
1004133/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn.preprocessing as pre
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
images = train_data.drop('label', 1)
observations, features = images.shape
pixel_width = int(np.sqrt(features))
X = images.as_matrix()
X_train = X.res... | code |
1004133/cell_10 | [
"text_plain_output_1.png"
] | import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.preprocessing as pre
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
images = train_data.drop('label', 1)
observations, features = images.shape
pixe... | code |
72108430/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.info() | code |
72108430/cell_30 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72108430/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(i... | code |
72108430/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.describe() | code |
72108430/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.head() | code |
72108430/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72108430/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris['Species'].value_counts() | code |
72108430/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_35 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn a... | code |
72108430/cell_31 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72108430/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72108430/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm') | code |
72108430/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend() | code |
16132902/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import tr... | code |
16132902/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotl... | code |
16132902/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
16132902/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
16132902/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pa... | code |
16132902/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
16132902/cell_5 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import L... | code |
129006129/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | !pip install transformers >/dev/null
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import... | code |
129006129/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import torch
test_data = pd.read_pickle('/kaggle/input/nlp-with-disaster-tweets-eda-cleaning-and-bert/test.pkl')
test_text = test_data.text_cleaned.apply(lambda x: x.lower()).values.tolist()
bert.eval()
testtext_embedding = []
with torch.no_grad():
for t in tqdm(test_te... | code |
129006129/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import torch
test_data = pd.read_pickle('/kaggle/input/nlp-with-disaster-tweets-eda-cleaning-and-bert/test.pkl')
test_text = test_data.text_cleaned.apply(lambda x: x.lower()).values.tolist()
bert.eval()
testtext_embedding = []
with torch.no_grad():
for t in tqdm(test_te... | code |
16119472/cell_13 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
test_df.take(5) | code |
16119472/cell_30 | [
"text_plain_output_1.png"
] | from nltk import pos_tag
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
from nltk.stem import WordNetLemmatizer
from pyspark.sql import SparkSession
import nltk
import string
spark = SparkSession.builder.appName('TestCSV').getO... | code |
16119472/cell_6 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5) | code |
16119472/cell_29 | [
"text_plain_output_1.png"
] | from nltk import pos_tag
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
from pyspark.sql import SparkSession
import string
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.c... | code |
16119472/cell_26 | [
"text_plain_output_1.png"
] | from nltk import pos_tag
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
from pyspark.sql import SparkSession
import string
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.c... | code |
16119472/cell_2 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
16119472/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16119472/cell_7 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns | code |
16119472/cell_18 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns
out_cols = [i for i in train_df.columns if i not in ['id', 'co... | code |
16119472/cell_28 | [
"text_plain_output_1.png"
] | from nltk import pos_tag
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
from pyspark.sql import SparkSession
import string
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.c... | code |
16119472/cell_15 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns
out_cols = [i for i in train_df.columns if i not in ['id', 'co... | code |
16119472/cell_31 | [
"text_plain_output_1.png"
] | from nltk import pos_tag
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
from nltk.stem import WordNetLemmatizer
from pyspark.sql import SparkSession
import nltk
import string
spark = SparkSession.builder.appName('TestCSV').getO... | code |
16119472/cell_24 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns
out_cols = [i for i in train_df.columns if i not in ['id', 'co... | code |
16119472/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns
out_cols = [i for i in train_df.columns if i not in ['id', 'co... | code |
16119472/cell_12 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('TestCSV').getOrCreate()
train_df = spark.read.csv('../input/train.csv', header=True)
test_df = spark.read.csv('../input/test.csv', header=True)
train_df.take(5)
train_df.columns
out_cols = [i for i in train_df.columns if i not in ['id', 'co... | code |
34120998/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !ls ../train_overlay/ | code |
34120998/cell_8 | [
"image_output_1.png"
] | from IPython.display import Image, display
from PIL import Image
display(Image(filename='../train_overlay/3046035f348012fdba6f7c53c4faa16e.png')) | code |
34120998/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/prostate-cancer-grade-assessment'
train = pd.read_csv(f'{path}/train.csv')
test = pd.read_csv(f'{path}/test.csv')
submission = pd.read_csv(f'{path}/sample_submission.csv')
suspicious = pd.read_csv(f'../input/suspicious-data-panda/suspicious_test_cases.csv')
data_dir = f'{path}/trai... | code |
90147986/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # Data processing, CSV file I/O (e.g. pd.read_csv)
krenth311 = pd.read_csv('../input/dataset/krenth311.csv')
krenth316 = pd.read_csv('../input/dataset/krenth316.csv')
merge = pd.concat([krenth311, krenth316])
merge.to_csv('merge.csv', i... | code |
90147986/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cufflinks as cf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates as md
import seaborn as sns
import plotly.graph_objs as go
import plotly
import cufflinks as cf
cf.set_config_file(offline=True)
import os | code |
90147986/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # Data processing, CSV file I/O (e.g. pd.read_csv)
krenth311 = pd.read_csv('../input/dataset/krenth311.csv')
krenth316 = pd.read_csv('../input/dataset/krenth316.csv')
merge = pd.concat([krenth311, krenth316])
merge.to_csv('merge.csv', index=False)
for i in heartrate:
... | code |
128006303/cell_6 | [
"text_html_output_1.png"
] | model1 = LinearRegression()
model1.fit(total_X, total_y)
model2 = LinearRegression()
model2.fit(men_X, men_y)
model3 = LinearRegression()
model3.fit(women_X, women_y) | code |
128006303/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error | code |
128006303/cell_8 | [
"text_plain_output_1.png"
] | model1 = LinearRegression()
model1.fit(total_X, total_y)
model2 = LinearRegression()
model2.fit(men_X, men_y)
model3 = LinearRegression()
model3.fit(women_X, women_y)
pred_total = model1.predict(total_X)
pred_men = model2.predict(men_X)
pred_women = model3.predict(women_X)
"""
MSE_total:16949.508877183063
MSE_men:109... | code |
128006303/cell_3 | [
"text_html_output_1.png"
] | train = pd.read_csv('/kaggle/input/population-projections/train.csv')
train.head() | code |
89137453/cell_21 | [
"text_plain_output_1.png"
] | from ipywidgets import interact, widgets
from tensorflow import keras
import math
import matplotlib.pyplot as plt
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data()
(in_train.shape, in_valid.shape, np.unique(out_train))
in_trai... | code |
89137453/cell_4 | [
"text_plain_output_1.png"
] | from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data() | code |
89137453/cell_6 | [
"image_png_output_1.png"
] | out_train | code |
89137453/cell_19 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow import keras
import math
import matplotlib.pyplot as plt
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data()
(in_train.shape, in_valid.shape, np.unique(out_train))
in_train = in_train / 255.0
in_valid = in_valid /... | code |
89137453/cell_18 | [
"image_output_1.png"
] | from tensorflow import keras
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data()
(in_train.shape, in_valid.shape, np.unique(out_train))
in_train = in_train / 255.0
in_valid = in_valid / 255.0
model = keras.Sequential(layers=[keras... | code |
89137453/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data()
(in_train.shape, in_valid.shape, np.unique(out_train))
in_train = in_train / 255.0
in_valid = in_valid / 255.0
model = keras.Sequential(layers=[keras... | code |
89137453/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(in_train, out_train), (in_valid, out_valid) = fashion_mnist.load_data()
(in_train.shape, in_valid.shape, np.unique(out_train))
in_train = in_train / 255.0
in_valid = in_valid / 255.0
model = keras.Sequential(layers=[keras... | code |
89137453/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
(in_train.shape, in_valid.shape, np.unique(out_train))
in_train = in_train / 255.0
in_valid = in_valid / 255.0
class_names = {index: cn for index, cn in enumerate(['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle bo... | code |
89137453/cell_5 | [
"image_output_1.png"
] | import numpy as np
(in_train.shape, in_valid.shape, np.unique(out_train)) | code |
18143474/cell_4 | [
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
df_source = pd.read_csv('../input/periodic-traffic-data/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_tim... | code |
18143474/cell_6 | [
"text_html_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
import datetime as dt
import lowess as lo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_source = pd.read_csv('../input/periodic-traffic-data/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, ... | code |
18143474/cell_7 | [
"text_html_output_1.png",
"image_output_1.png"
] | from pandas import DataFrame
import datetime as dt
import lowess as lo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_source = pd.read_csv('../input/periodic-traffic-data/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, ... | code |
18143474/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import datetime as dt
import pandas as pd
df_source = pd.read_csv('../input/periodic-traffic-data/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, inplace=True)
df_source_time = df_source.copy()
df_source_time['rep_time'] = df_source_time['rep_date'].... | code |
18143474/cell_5 | [
"image_output_11.png",
"text_html_output_10.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"image_output_5.png",
"image_output_7.png",
"text_html_output_9.png",
"image_output_4.png",
"image_output_8.png",
"text_html_output_1.png",
... | from pandas import DataFrame
import datetime as dt
import lowess as lo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_source = pd.read_csv('../input/periodic-traffic-data/periodic_traffic.csv')
df_source['rep_date'] = pd.to_datetime(df_source['_time'])
df_source.drop(['_time'], axis=1, ... | code |
330145/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
330145/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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
330145/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
2020968/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
columns = ['SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train[columns].describe(include='all', percentiles=[]) | code |
2020968/cell_23 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
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