path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128046727/cell_52 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_45 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_49 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_51 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_59 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import glob
import imageio
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex',... | code |
128046727/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diabetes', 'glaucoma', 'cataract', 'amd', 'hypertension', 'm... | code |
128046727/cell_47 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diabetes', 'glaucoma', 'cataract', 'amd', 'hypertension', 'm... | code |
128046727/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_43 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_46 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_di... | code |
128046727/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
128046727/cell_5 | [
"image_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
print(dirname) | code |
128046727/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_excel('/kaggle/input/ocular-disease-recognition-odir5k/ODIR-5K/ODIR-5K/data.xlsx', engine='openpyxl')
data_df.columns = ['id', 'age', 'sex', 'left_fundus', 'right_fundus', 'left_diagnosys', 'right_diagnosys', 'normal', 'diab... | code |
106190796/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
round(df.isna().sum() / df.shape[0] * 100, 2) | code |
106190796/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.head() | code |
106190796/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape | code |
106190796/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
sns.countplot(df.Transported) | code |
106190796/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
round(df.isna().sum() / df.shape[0] * 100, 2)
df.drop(['PassengerId', 'Ca... | code |
106190796/cell_15 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
round(df.isna().sum() / df.shape[0] * 100, 2)
df.drop(['PassengerId', 'Ca... | code |
106190796/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5) | code |
106190796/cell_17 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
round(df.isna().sum() / df.shape[0] * 100, 2)
df.drop(['PassengerId', 'Cabin', 'Name'], axis=1, inplace=Tr... | code |
106190796/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.shape
round(df.isna().sum() / df.shape[0] * 100, 2)
for col in df.select_dtypes(include='object'):
print(f'{col} -- > {df[col].unique()} \nTotal Unique Values --> {df[col].nunique()}\n-----------... | code |
106190796/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.sample(5)
df.describe(percentiles=[i / 10 for i in range(1, 10)]) | code |
34136379/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_34 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sn... | code |
34136379/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_33 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sn... | code |
34136379/cell_28 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
df['ArtisT_Name'].unique() | code |
34136379/cell_43 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
c... | code |
34136379/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import statsmodels.formula.api as smf
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sn... | code |
34136379/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False)
import seaborn as sns
corr = df.corr()
ax = sns.heatmap(corr, vmin=-1, vmax=1, center=0, cmap=sns.diverging_palette(20, 220, n=200), square=True) | code |
34136379/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/top-50-spotify/top50.csv', encoding='latin-1')
df.sort_values(by=['Popularity'], ascending=False) | code |
128008136/cell_13 | [
"text_plain_output_1.png"
] | !pip install -q -U segmentation-models-pytorch albumentations > /dev/null
import segmentation_models_pytorch as smp | code |
128008136/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
DATA_DIR = '/kaggle/input/cvcclinicdb'
metadata_df = pd.read_csv(os.path.join(DATA_DIR, 'metadata.csv'))
metadata_df = metadata_df[['frame_id', 'png_image_path', 'png_mask_path']]
metadata_df['png_image_path'] = metadata_df['png_image_path'].apply(lambda img_pth: os.path.join(DATA_DIR, i... | code |
128008136/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
DATA_DIR = '/kaggle/input/cvcclinicdb'
metadata_df = pd.read_csv(os.path.join(DATA_DIR, 'metadata.csv'))
metadata_df = metadata_df[['frame_id', 'png_image_path', 'png_mask_path']]
metadata_df['png_image_path'] = metadata_df['png_image_path'].apply(lambda img_pth: os.path.join(DATA_DIR, i... | code |
128008136/cell_15 | [
"text_plain_output_1.png"
] | !pip install torchsummary
from torchsummary import summary
summary(model,(3, 288, 384)) | code |
331059/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
def logloss_err(y, p):
N = len(p)
err = -1 / N * np.sum(y * np.log(p))
return err
p = np.array([[0.2, 0.8], [0.7, 0.3]])
y = np.array([[0, 1], [1, 0]])
logloss_err(y, p) | code |
331059/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
import numpy as np # linear algebra
import os #joining filepath
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def logloss_err(y, p):
N = len(p)
err = -1 / N * np.sum(y * np.log(p))
return err
p = np.array([[0.2, 0.8], [0.7, 0.3]])
y =... | code |
32068117/cell_21 | [
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from cleantext import clean
from sentence_transformers import SentenceTransformer
from summarizer import Summarizer
from tqdm import tqdm
import faiss
import numpy as np
import os
import pandas as pd
from sentence_transformers import SentenceTransformer
import faiss
from tqdm import tqdm
from summarizer import ... | code |
32068117/cell_2 | [
"text_plain_output_1.png"
] | import os
from sentence_transformers import SentenceTransformer
import faiss
from tqdm import tqdm
from summarizer import Summarizer
import scipy.spatial
import pandas as pd
import numpy as np
import re
import json
import time
import os
from cleantext import clean
for dirname, _, filenames in os.walk('/kaggle/input'):... | code |
32068117/cell_1 | [
"text_plain_output_1.png"
] | !pip install torch===1.4.0 torchvision===0.5.0 -f https://download.pytorch.org/whl/torch_stable.html
!pip install clean-text
!pip install -U sentence-transformers
!pip install tqdm
!pip install faiss-cpu --no-cache
!pip install bert-extractive-summarizer
!pip install spacy==2.1.3 | code |
17118681/cell_20 | [
"text_plain_output_1.png"
] | print('Salvando modelo em arquivo \n')
mp = '.\\mnist_model.h5'
model.save(mp) | code |
17118681/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | batch_size = 128
max_epochs = 50
print('Iniciando treinamento... ') | code |
17118681/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
import seaborn as sns
import os
from matplotlib import pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | code |
17118681/cell_24 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
mp = '.\\mnist_model.h5'
model.save(mp)
unknown = np.zeros(shape=(28, 28), dtype=np.float32)
for row in range(5, 23):
unknown[row][9] = 180
for rc in range(9, 19):
unknown[rc][rc] = 250
unknown = unknown.reshape(1, 28, 28, 1... | code |
17118681/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
mp = '.\\mnist_model.h5'
model.save(mp)
print('Usando o modelo para previsão de dígitos para a imagem: ')
unknown = np.zeros(shape=(28, 28), dtype=np.float32)
for row in range(5, 23):
unknown[row][9] = 180
for rc in range(9, 19)... | code |
90133914/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90133914/cell_6 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
from scipy.stats import norm
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query =... | code |
90133914/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n `bigquery-public-data.bitcoin_... | code |
90133914/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90133914/cell_5 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELE... | code |
34120483/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
34120483/cell_21 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual ... | code |
34120483/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud
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 #what is seaborn
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_... | code |
34120483/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.r... | code |
34120483/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample.head() | code |
34120483/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_actual = pd.read_csv('../input/nlp-getting-started/test.csv') | code |
34120483/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample['length'] = np.NaN
for i in range(0, len(df_sample['text'])):
df_sample['length'][i] = len(df_s... | code |
34120483/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
34120483/cell_7 | [
"text_html_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 #what is seaborn
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample['length'] = np.NaN
for i i... | code |
34120483/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample['length'] = np.NaN
for i in range(0, len(df_sample['text'])):
df_sample['length'][i] = len(df_s... | code |
34120483/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
34120483/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual ... | code |
34120483/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud
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 #what is seaborn
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_... | code |
34120483/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_actual = pd.read_csv('../input/nlp-getting-started/test.csv')
df_actual.head() | code |
34120483/cell_24 | [
"image_output_1.png"
] | from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.r... | code |
34120483/cell_14 | [
"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)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample['length'] = np.NaN
for i in range(0, len(df_sample['text'])):
df_sample['length'][i] = len(df_s... | code |
34120483/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual ... | code |
34120483/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,make_scorer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual ... | code |
34120483/cell_12 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
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 re
import seaborn as sns #what is seaborn
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test... | code |
34120483/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sample = pd.read_csv('/kaggle/input/train.csv')
df_actual = pd.read_csv('/kaggle/input/test.csv')
df_sample['target'].value_counts() | code |
33097052/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pybtex.database.input import bibtex
import pandas as pd | code |
1003788/cell_25 | [
"text_plain_output_1.png",
"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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascendin... | code |
1003788/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull(... | code |
1003788/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
all_data =... | code |
1003788/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull(... | code |
1003788/cell_29 | [
"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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascendin... | code |
1003788/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull(... | code |
1003788/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull(... | code |
1003788/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull(... | code |
1003788/cell_32 | [
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
all_data =... | code |
1003788/cell_28 | [
"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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascendin... | code |
1003788/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull(... | code |
1003788/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
all_data = all_data.replace({'Utilities': {'AllPub': 1, 'NoSeWa': ... | code |
1003788/cell_17 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull(... | code |
1003788/cell_31 | [
"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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascendin... | code |
1003788/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull(... | code |
1003788/cell_27 | [
"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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
total = train.isnull().sum().sort_values(ascendin... | code |
331419/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
... | code |
73074336/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df | code |
73074336/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape | code |
73074336/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.head() | code |
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