path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32062359/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from cord import ResearchPapers
research_papers = ResearchPapers.load() | code |
32062359/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from cord import ResearchPapers
research_papers = ResearchPapers.load()
help(research_papers.search) | code |
32062359/cell_17 | [
"text_plain_output_1.png"
] | from langdetect import detect
from nltk.tokenize import sent_tokenize,word_tokenize
from tqdm import tqdm
import pandas as pd
import pandas as pd
keywordlist = ['inhibitor']
def loopsearch(keywordlist, researchpaperfu):
alldataframeco = pd.DataFrame()
alldataframenoco = pd.DataFrame()
allcopid = []
... | code |
32062359/cell_10 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from cord import ResearchPapers
from tqdm import tqdm
import pandas as pd
import pandas as pd
keywordlist = ['inhibitor']
research_papers = ResearchPapers.load()
help(research_papers.search)
def loopsearch(keywordlist, researchpaperfu):
alldataframeco = pd.DataFrame()
alldataframenoco = pd.DataFrame()
... | code |
2019285/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2019285/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2019285/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2019285/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2019285/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2019285/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([... | code |
2019285/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler() | code |
2019285/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_livin... | code |
2019285/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2019285/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2019285/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
2005289/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
an... | code |
2005289/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.uniqu... | code |
2005289/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
rating_df.shape | code |
2005289/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes | code |
2005289/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
an... | code |
2005289/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum() | code |
2005289/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.head() | code |
2005289/cell_15 | [
"text_html_output_1.png"
] | import collections
import itertools
import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
... | code |
2005289/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape | code |
2005289/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
an... | code |
2005289/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.uniqu... | code |
2005289/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
an... | code |
2005289/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.head() | code |
2036880/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes ... | code |
2036880/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes ... | code |
2036880/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Imputer
from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processi... | code |
2036880/cell_16 | [
"image_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes ... | code |
2036880/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import matplotlib.pyplot as plt
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and ana... | code |
74042371/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/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 |
74042371/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74042371/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74042371/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
1009060/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
tr... | code |
1009060/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
train = pd.read_csv(directory + 'train.... | code |
1009060/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from subprocess import check_output
print(ch... | code |
1009060/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
train = pd.read_csv(directory + 'train.... | code |
130021146/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import os
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ratio=(0.8, 0.1, 0.1)) | code |
130021146/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.image as mping
import matplotlib.pyplot as plt
import os
import random
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ... | code |
130021146/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing import image_dataset_from_directory
import matplotlib.image as mping
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
im... | code |
130021146/cell_7 | [
"text_plain_output_1.png"
] | !pip install split_folders | code |
130021146/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing import image_dataset_from_directory
import tensorflow as tf
from tensorflow.keras.preprocessing import image_dataset_from_directory
train_dir = './o... | code |
130021146/cell_15 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.preprocessing import image_dataset_from_directory
train_dir = './output/train'
test_dir = './output/test'
val_dir = './output/val'
train_data = image_dataset_from_directory(train_dir, label_mode='categorical', image_size=(299... | code |
130021146/cell_16 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
import tensorflow as tf
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
mod... | code |
130021146/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
import tensorflow as tf
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
mod... | code |
130021146/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.image as mping
import matplotlib.pyplot as plt
import os
import random
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ... | code |
1003458/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv') | code |
106195418/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
... | code |
106195418/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_... | code |
106195418/cell_4 | [
"image_output_1.png"
] | !pip install pytorch_forecasting | code |
106195418/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import random
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.us... | code |
106195418/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
... | code |
106195418/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_... | code |
106195418/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib... | code |
106195418/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_... | code |
106195418/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
... | code |
106195418/cell_5 | [
"image_output_1.png"
] | !pip install holidays | code |
74046791/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('insurance.csv')
data.head() | code |
1004487/cell_4 | [
"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 sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
g... | code |
1004487/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre... | code |
1004487/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import sqlite3
import matplotlib.pyplot as plt
import scipy.stats as stats
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004487/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre... | code |
1004487/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre... | code |
1004487/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre... | code |
34123490/cell_7 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35) | code |
34123490/cell_17 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35)
avg_weights1 = [29, 31, 28, 33, 31, 34, 32, 20, 32, 28, 27, 26, 30, 31, 34, 30]
stats.ttest_ind(avg_weights, avg_weights1)
before_meta = [68, 45, 46, 34, 23, 67, 80, 120, 34... | code |
34123490/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35)
avg_weights1 = [29, 31, 28, 33, 31, 34, 32, 20, 32, 28, 27, 26, 30, 31, 34, 30]
stats.ttest_ind(avg_weights, avg_weights1) | code |
88086201/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklea... | code |
88086201/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df.info() | code |
88086201/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
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 matplotlib.... | code |
88086201/cell_33 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.r... | code |
88086201/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_pa... | code |
88086201/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
def joinSyllable(c):
out = ''
for doc in c:
out += ' '.... | code |
88086201/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df.head() | code |
88086201/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
d... | code |
88086201/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawTe... | code |
88086201/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.r... | code |
88086201/cell_24 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (13, 9)})
palette = sns.hls_palette(k, l=0.4, s=0.9)
sns.scatterplot(X_embedded[:, 0], X_embedded[:, 1], hue=y_pred, legend='full', palette=palette)
plt.title('t-SNE with Kmeans Labels')
plt.savefig('improved_cluster_tsne.png')
plt... | code |
88086201/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklea... | code |
88086201/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df[df['y'] == 1] | code |
88086201/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.r... | code |
90124932/cell_9 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras_preprocessing.image import ImageDataGenerator
import math
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
im... | code |
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