path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17118075/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 're... | code |
17118075/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred = lr.predict(X_test)
pred
from sklearn.metrics import classification_report
report = classification_repo... | code |
17118075/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'restec... | code |
17118075/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart.csv')
df.info() | code |
17118075/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred = lr.predict(X_test)
pred | code |
17118075/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
17118075/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
plt.figure(figsize=(10, 7))
sns.set_style('whitegrid')
sns.countplot('target', data=df) | code |
17118075/cell_19 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 're... | code |
17118075/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart.csv')
df.describe() | code |
17118075/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
sns.set_style('... | code |
17118075/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train) | code |
17118075/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart.csv')
df.shape | code |
17118075/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.ge... | code |
17118075/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])
dataset.head() | code |
17118075/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'restecg', '... | code |
17118075/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
plt.figure(figsize=(12, 7))
sns.heatmap(df.corr(), cmap='coolwarm', annot=True) | code |
2006619/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
print(' : train : test')
print('rows :', nrow_train, ':', nrow_test)
print('perc :', round(nrow_train * 100 / sum), ' :',... | code |
2006619/cell_2 | [
"text_plain_output_1.png"
] | #Check the dataset sizes(in MB)
!du -l ../input/* | code |
2006619/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x = train.iloc[:, 2:].sum()
plt.figure(figsize=(8, 4))
ax = sns.barplot(x.in... | code |
2006619/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
nrow_train = train.shape[0]
nrow_test = test.shape[0]
sum = nrow_train + nrow_test
x=train.iloc[:,2:].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.i... | code |
2006619/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.tail(10) | code |
74067198/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.pr... | code |
74067198/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_df = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
train_df.describe() | code |
74067198/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_df = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.cs... | code |
74067198/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import pandas a... | code |
74067198/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
fr... | code |
74067198/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_df = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
train_df.isna().sum().describe() | code |
90140085/cell_9 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.... | code |
90140085/cell_6 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.text == '日足（CSV）':
... | code |
90140085/cell_2 | [
"text_plain_output_1.png"
] | !pip install requests
!pip install BeautifulSoup4 | code |
90140085/cell_11 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.... | code |
90140085/cell_7 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.... | code |
90140085/cell_8 | [
"text_html_output_2.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.... | code |
90140085/cell_15 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/mar... | code |
90140085/cell_16 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/mar... | code |
90140085/cell_10 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
url = 'https://info.ctfx.jp/service/market/data_download.html'
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
DIR = 'https://info.ctfx.jp/service/market/'
table0 = soup.find_all('a')
table1 = []
for item in table0:
if item.... | code |
1009667/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from glob import glob
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.DataFrame([dict(path=c_path, image_name=os.path.basename(c_path), type_cat=os.path.basename(os.path.dirname(c_path))) for c_path in glob('../input/train/*/*')])
print('Total Samples', train_df.shape[0... | code |
1009667/cell_3 | [
"text_html_output_1.png"
] | from glob import glob
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.DataFrame([dict(path=c_path, image_name=os.path.basename(c_path), type_cat=os.path.basename(os.path.dirname(c_path))) for c_path in glob('../input/train/*/*')])
train_df.sample(3)
test_df = pd.DataFr... | code |
1009667/cell_5 | [
"text_html_output_1.png"
] | from glob import glob
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.DataFrame([dict(path=c_path, image_name=os.path.basename(c_path), type_cat=os.path.basename(os.path.dirname(c_path))) for c_path in glob('../input/train/*/*')])
train_df.sample(3)
test_df = pd.DataFr... | code |
73080780/cell_9 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_2... | from sklearn import compose
from sklearn import impute
from sklearn import metrics
from sklearn import model_selection
from sklearn import pipeline
from sklearn import preprocessing
import lightgbm as lgbm
import numpy as np
import pandas as pd
import random
import xgboost as xgb
import glob
import pandas as... | code |
73080780/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import model_selection
import numpy as np
import pandas as pd
import random
FOLDS = 10
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
kfold = model_selection.KFold(n_splits=FOLDS, shuffle=True, random_state=42)
df_train['fold'] = -1
for fold, (_, valid_idx) in e... | code |
18159892/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
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_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def display_train_images(df, col=3, row=3):
tol = 30 # initializing... | code |
18159892/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head() | code |
18159892/cell_34 | [
"text_plain_output_1.png"
] | from keras import layers
from keras.applications import DenseNet121
from keras.models import Sequential
from keras.optimizers import Adam
densenet = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
def build_model():
model = Sequential()
model.add(densenet)
model.add(layers... | code |
18159892/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd ... | code |
18159892/cell_16 | [
"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)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df['diagnosis'].value_counts().plot.bar()
plt.title('Visualization of DR Trainig Dataset')
plt.xlabel('DR Diagnosis Type... | code |
18159892/cell_35 | [
"text_plain_output_1.png"
] | from keras import layers
from keras.applications import DenseNet121
from keras.callbacks import Callback, ModelCheckpoint
from keras.models import Sequential
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import cohen_kappa_score, accuracy_score
BA... | code |
18159892/cell_31 | [
"image_output_1.png"
] | from keras.applications import DenseNet121
densenet = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) | code |
18159892/cell_24 | [
"image_output_1.png"
] | import cv2
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_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
def display_train_images(df, col=3, row=3):
tol = 30 # initializing... | code |
18159892/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df['diagnosis'].value_counts() | code |
32068582/cell_6 | [
"text_plain_output_1.png"
] | from scipy.optimize import curve_fit
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
popDict = {'Afghanistan_NONE': 34656032, 'Albania_NONE': 2876101, 'Algeria_NONE': 40606052, 'Andorra_NONE': 77281, 'Angola_NONE': 28813463, 'Ant... | code |
32068582/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 |
50221500/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.head() | code |
50221500/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50221500/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.head() | code |
50221500/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.head() | code |
18143939/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import bs4
import pandas as pd
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):... | code |
18143939/cell_9 | [
"image_output_1.png"
] | import bs4
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):
soup = bs4.Beaut... | code |
18143939/cell_20 | [
"text_html_output_1.png"
] | from nltk.stem.porter import PorterStemmer
from wordcloud import WordCloud
import bs4
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import requests
import sklearn.feature_extraction.text as skt
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/... | code |
18143939/cell_6 | [
"text_plain_output_1.png"
] | import bs4
import pandas as pd
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):... | code |
18143939/cell_7 | [
"image_output_1.png"
] | import bs4
import pandas as pd
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):... | code |
18143939/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.stem.porter import PorterStemmer
from wordcloud import WordCloud
import bs4
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import requests
import sklearn.feature_extraction.text as skt
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/... | code |
18143939/cell_10 | [
"text_plain_output_1.png"
] | import bs4
import pandas as pd
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):... | code |
18143939/cell_12 | [
"text_plain_output_1.png"
] | import bs4
import pandas as pd
import requests
r1 = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r2 = requests.get('https://www.washingtonpost.com/politics/2019/08/01/transcript-night-second-democratic-debate/')
def parse_requests(r, night=None):... | code |
105198632/cell_4 | [
"text_plain_output_1.png"
] | j = 0
for i in range(1, 31):
j = j + i
i = 0
j = 0
while i <= 30:
j = j + i
i = i + 1
print(j) | code |
105198632/cell_2 | [
"text_plain_output_1.png"
] | j = 0
for i in range(1, 31):
j = j + i
print(j) | code |
309674/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from subprocess import check_output
comments = pd.read_csv('../input/comment.csv')
likes = pd.read_... | code |
74045780/cell_9 | [
"text_plain_output_1.png",
"image_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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.value_counts(ascending=True) | code |
74045780/cell_4 | [
"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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes | code |
74045780/cell_6 | [
"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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.plot() | code |
74045780/cell_2 | [
"text_html_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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.head() | code |
74045780/cell_1 | [
"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 matplotlib as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
df = pd.read_csv('../input/amr... | code |
74045780/cell_7 | [
"text_plain_output_1.png",
"image_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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True) | code |
74045780/cell_8 | [
"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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.plot() | code |
74045780/cell_3 | [
"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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.describe() | code |
74045780/cell_10 | [
"text_html_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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.value_counts(ascending=True)
df.... | code |
74045780/cell_5 | [
"text_plain_output_1.png",
"image_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 matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape | code |
128044853/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import nltk
import os
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import lightgbm as lgb
import nltk
nltk.download('punkt')
nl... | code |
128044853/cell_7 | [
"text_html_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import joblib
import lightgbm as lgb
import nltk
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O ... | code |
128044853/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import lightgbm as lgb
import nltk
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_c... | code |
128044853/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
fro... | code |
128044853/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import nltk
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
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.model... | code |
122256136/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_6 | [
"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
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
sns.countp... | code |
122256136/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
data.head() | code |
122256136/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 |
122256136/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_8 | [
"text_html_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
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_14 | [
"text_html_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
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_10 | [
"text_html_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
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
122256136/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
pd.set_option('display.max_columns', None)
plt.rcParams['figure.figsize'] = (8, 6)
n_missing... | code |
2010673/cell_13 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words ... | code |
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