path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130007285/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130007285/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
sorted_df.head() | code |
130007285/cell_22 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price'... | code |
130007285/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
selected_columns = ['Departure City', 'Arrival City', 'Flight Duration', 'Delay Minutes', 'Booking Class']
df_selected = df[selected_columns]
df_selected | code |
130007285/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df = df[df['Churned'] == False]
filtered_df.head() | code |
130007285/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.info() | code |
17143749/cell_13 | [
"text_plain_output_2.png"
] | chunk_iter = _smallstruct.groupby(['molecule_name'])
pool = mp.Pool(4)
funclist = []
for df in tqdm(chunk_iter):
f = pool.apply_async(compute_all_yukawa, [df[1]])
funclist.append(f)
result = []
for f in tqdm(funclist):
result.append(f.get(timeout=120))
smallstruct2 = pd.concat(result) | code |
17143749/cell_11 | [
"text_html_output_1.png"
] | smallstruct1.head(10) | code |
17143749/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
def compute_all_yukawa(x):
return x.apply(compute_yukawa_matrix, axis=1, x2=x)
def compute_yukawa_matrix(x, x2):
notatom = x2[x2.atom_index != x['atom_index']].reset_index(drop=True)
atom = x[['x', 'y', 'z']]
charge = x[['nuclear_charge']]
notatom['dist'] = ((notatom[['x', 'y', '... | code |
17143749/cell_14 | [
"text_html_output_1.png"
] | smallstruct2.head(10) | code |
17143749/cell_10 | [
"text_plain_output_1.png"
] | smallstruct1 = _smallstruct.groupby('molecule_name').apply(compute_all_yukawa) | code |
50241935/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.info()
data.describe() | code |
50241935/cell_11 | [
"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)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(... | code |
50241935/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import scale
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram, cut_tree
import os
for dirname, _, filenames in os.walk('/ka... | code |
50241935/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_18 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%... | code |
50241935/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 /... | code |
50241935/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.head(5) | code |
50241935/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 /... | code |
50241935/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data... | code |
50241935/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0] | code |
105210534/cell_42 | [
"image_output_1.png"
] | l = [('1', 1), ('2', 2), ('3', 3)]
max(l) | code |
105210534/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_da... | code |
105210534/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset... | code |
105210534/cell_25 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_target = test_data[187].value_counts()
test_target | code |
105210534/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', h... | code |
105210534/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', h... | code |
105210534/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.se... | code |
105210534/cell_26 | [
"image_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_da... | code |
105210534/cell_41 | [
"image_output_1.png"
] | l = [('1', 1), ('2', 2), ('3', 3)]
if max(l):
print(l) | code |
105210534/cell_2 | [
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105210534/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
subset | code |
105210534/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_da... | code |
105210534/cell_50 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heart... | code |
105210534/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
train_target | code |
105210534/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_da... | code |
105210534/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.se... | code |
105210534/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
plt.bar(train_target.index, train_target.values, color=sb.color_palette()[4]) | code |
105210534/cell_46 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
from sklearn.utils import resample
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(20... | code |
105210534/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset... | code |
105210534/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value... | code |
105210534/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', h... | code |
48165941/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_link = pd.read_csv('../input/dataset/datasets.csv')
df = df_link.copy()
df = df.dropna(axis=0, subset=['Source'])
df.head() | code |
48165941/cell_6 | [
"image_output_1.png"
] | from skimage import io
import matplotlib.pyplot as plt
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
plt.imshow(image) | code |
48165941/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 |
48165941/cell_7 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
for i in range(len(list)):
print(list[i]) | code |
48165941/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import cv2
import imageio
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import num... | code |
48165941/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage import io
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria... | code |
48165941/cell_15 | [
"text_plain_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import cv2
import imageio
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import num... | code |
48165941/cell_3 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_link = pd.read_csv('../input/dataset/datasets.csv')
df_link.head() | code |
48165941/cell_10 | [
"text_html_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import i... | code |
48165941/cell_12 | [
"text_html_output_1.png"
] | !pip install imutils | code |
48165941/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
df_link = pd.read_csv('../input/dataset/datasets.csv')
df = df_link.copy()
df = df.dropna(axis=0, subset=['Source'])
import requests
from io import BytesIO
from PIL import Image
for i in range(100):
r = r... | code |
72103063/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull()... | code |
72103063/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_potable.isnull().sum()
df_potable.isnull().su... | code |
72103063/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_potable.isnull().sum() | code |
72103063/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
d... | code |
72103063/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
print('number of rows: ', df.shape[0])
print('number of column: ', df.shape[1])
df.Potability.value_counts() | code |
72103063/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_notpotable.isnul... | code |
72103063/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 |
72103063/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull()... | code |
72103063/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum() | code |
72103063/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull()... | code |
72103063/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.head() | code |
72103063/cell_22 | [
"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)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.is... | code |
72103063/cell_10 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable... | code |
72103063/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df.Potability.value_counts() | code |
72103063/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum() | code |
122261656/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
print(data[44505, :] == data[46157, :]) | code |
122261656/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
data.head() | code |
122261656/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def Jaccard(i, j):
iData = data[i, :]
jDat... | code |
122261656/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
print(data)
np.random.seed(0)
n = 30
m = data.shape[0]
print(m)
length = data.shape[1]
print(length)
signMatrix = np.zeros((n, m)) | code |
122261656/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import jaccard_score
from tqdm import tqdm
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np... | code |
122261656/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
for i in range(n):
seq = np.arange(0, length)
... | code |
122261656/cell_16 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucke... | code |
122261656/cell_17 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucke... | code |
122261656/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucketAllocate(r, b):
Bucket = {}
for ... | code |
122261656/cell_12 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucke... | code |
88094115/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model i... | code |
88094115/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
df.head() | code |
88094115/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)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x | code |
88094115/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model i... | code |
88094115/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 |
88094115/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
y = df.iloc[:, 1].values
y | code |
88094115/cell_8 | [
"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)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
plt.scatter(x, y)
plt.show() | code |
88094115/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model i... | code |
88094115/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1]... | code |
88094115/cell_14 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model i... | code |
88094115/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model i... | code |
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