path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88087414/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def c... | code |
88087414/cell_39 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
print(s)
print('0... | code |
88087414/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_... | code |
88087414/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_18 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_... | code |
88087414/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
data_train['text... | code |
88087414/cell_16 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
data_train.head(... | code |
88087414/cell_38 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_35 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/trai... | code |
88087414/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
print('the max l... | code |
88087414/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
print('null values for train data : ')
print(data_train.isna().sum())
print('... | code |
88087414/cell_37 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train.head() | code |
34121960/cell_6 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.head(3) | code |
34121960/cell_2 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
"text_plain_output_31.png",
"text_plain_output... | 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 |
34121960/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
listOfLists1 = []
with open('../input/CORD-19-research-challenge/json_schema.txt') as f:
for line in f:
inner_list = [line.strip() for line in line.split(' split cha... | code |
122253082/cell_9 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df['Date'] = pd.to_datetime(df['Date'])
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(df['Date'], df['Average'])
ax.set_title('Tomato Weight Time Series')
ax.set_xlabel('... | code |
122253082/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df.head() | code |
122253082/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
import plotly.express as px
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
grouped = df.groupby(['Year', 'Month'])['Average'].mean().reset_index()
fig = px.l... | code |
122253082/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df.info() | code |
89126605/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from moviepy.editor import *
clip = VideoFileClip('./Rick Astley - Never Gonna Give You Up (Official Music Video).mp4')
clip1 = clip.subclip(0, 5)
clip2 = clip.subclip(60, 65)
final = concatenate_videoclips([clip1, clip2])
final.write_videofile('merged.mp4') | code |
89126605/cell_2 | [
"text_plain_output_1.png"
] | !pip install moviepy
!pip install pytube | code |
89126605/cell_3 | [
"text_plain_output_1.png"
] | import pytube
import pytube
url = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'
youtube = pytube.YouTube(url)
video = youtube.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()
video.download() | code |
2015996/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.re... | code |
2015996/cell_2 | [
"text_html_output_1.png"
] | 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.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.re... | code |
2015996/cell_3 | [
"text_html_output_1.png"
] | 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.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.re... | code |
2015996/cell_5 | [
"text_plain_output_1.png",
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_res... | code |
90120064/cell_21 | [
"image_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
i... | code |
90120064/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape | code |
90120064/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.describe() | code |
90120064/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalys... | code |
90120064/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False) | code |
90120064/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.head() | code |
90120064/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from skl... | code |
90120064/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.info() | code |
90102236/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BAT... | code |
90102236/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = Im... | code |
90102236/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
print('Test Set Size:', test.shape) | code |
90102236/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test.head() | code |
90102236/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow import keras
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary() | code |
90102236/cell_7 | [
"text_html_output_1.png"
] | import os
test_path = '../input/histopathologic-cancer-detection/test'
print('Test Images:', len(os.listdir(test_path))) | code |
90102236/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
submission = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
submission.head() | code |
90102236/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-... | code |
90102236/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-... | code |
90102236/cell_12 | [
"text_html_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BAT... | code |
34144500/cell_21 | [
"text_plain_output_1.png"
] | import json
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
id_to_cat | code |
34144500/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.c... | code |
34144500/cell_33 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nlt... | code |
34144500/cell_40 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nlt... | code |
34144500/cell_39 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nlt... | code |
34144500/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.c... | code |
34144500/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.c... | code |
34144500/cell_37 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nlt... | code |
34144500/cell_36 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nlt... | code |
32062754/cell_4 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')... | code |
32062754/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')
df_global = pd.read_csv('/kaggle/input/global-hospital-be... | code |
32062754/cell_3 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')... | code |
32062754/cell_5 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')... | code |
18100887/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data) | code |
18100887/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label) | code |
18100887/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d) | code |
18100887/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label) | code |
18100887/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label) | code |
121151851/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Gender'].value_counts() | code |
121151851/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'u... | code |
121151851/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.head() | code |
121151851/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'u... | code |
121151851/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
genders = np.array([36, 55, 9])
mylabels = ['Female 36%', 'Male 55%', 'Firm 9%']
myexplode = [0.2, 0, 0]
plt.pie(genders, labels=mylabels, explode=myexplode, shadow=True)
plt.show() | code |
121151851/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360['Country'].unique() | code |
121151851/cell_29 | [
"text_plain_output_1.png"
] | state_cf = state_percent.round() | code |
121151851/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum() | code |
121151851/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360['State'].unique() | code |
121151851/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'u... | code |
121151851/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum() | code |
121151851/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum() | code |
121151851/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100 | code |
121151851/cell_17 | [
"text_plain_output_1.png"
] | print(round(55.38))
print(round(35.89))
print(round(8.71)) | code |
121151851/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['State'].value_counts() | code |
121151851/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Entity'].value_counts() | code |
121151851/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Country'].value_counts() | code |
121151851/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum() | code |
121151851/cell_27 | [
"image_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'u... | code |
121151851/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.info() | code |
122251358/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
import operator
import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_df['prompt'].apply(lambda x: x.lower())
... | code |
122251358/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_df['prompt'].apply(lambda x: x.lower())
print(f'Train shape: {train_df.shape}')
train_df.head() | code |
122251358/cell_15 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
from transformers import AutoTokenizer
import operator
def build_vocab(sentences, verbose=True):
"""
Builds a vocabulary dictionary where keys are the unique words in our sentences and
the values are the word counts.
:param sentences: list of list of ... | code |
122251358/cell_17 | [
"text_plain_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
from transformers import AutoTokenizer
import operator
import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_d... | code |
16136181/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month',... | code |
16136181/cell_25 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'pro... | code |
16136181/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_outp... | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.head() | code |
16136181/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate'... | code |
16136181/cell_30 | [
"image_output_11.png",
"text_plain_output_5.png",
"image_output_14.png",
"text_plain_output_4.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
... | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'pro... | code |
16136181/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate'... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.