path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068850/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
32068850/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sbm = pd.read_csv('/kaggle/input/college-salaries/degrees-that-pay-back.csv').set_index('Undergraduate Major')
sbc = pd.read_csv('/kaggle/input/college-salaries/salaries-by-college-type.csv').set_index('School Name')
sbr = pd.read_csv('/kaggle/inpu... | code |
1007484/cell_11 | [
"text_html_output_1.png"
] | from scipy import stats, optimize
import numpy as np
import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for s... | code |
1007484/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for seed in range(2, 17):
res... | code |
1007484/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
df.head() | code |
90152893/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 |
90152893/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df = pandas.read_csv('../input/water-potability/water_potability.csv')
df.describe() | code |
73078417/cell_21 | [
"image_output_1.png"
] | import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
air_passengers = pd.read_csv('../input/air-passengers/AirPassengers.csv', header=0, parse_dates=[0], names=['Month', 'Passengers'], index_col=0)
air_passengers.plot() | code |
73078417/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(result_curve)
plt.plot(acf_result)
plt.axhline(y=0, linestyle='--')
plt.axhline(y=-1.96 / np.sqrt(len(result_curve)), ... | code |
73078417/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
pylab.rcParams['figure.figsize'] = (10, 6)
import pandas as pd
import numpy as np | code |
73078417/cell_11 | [
"text_plain_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(result_curve)
grid = np.linspace(0, 100, 1000)
sin5 = np.sin(grid)
result_curve = sin5
plt.plot(grid, result_curve) | code |
73078417/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(resul... | code |
73078417/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
import numpy as np
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
plt.plot(grid, result_curve) | code |
73078417/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(resul... | code |
73078417/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(resul... | code |
73078417/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
diff_f = f.Open - f.Open.shift()
diff_f.plot()
diff_f.dropna(inplace=True) | code |
73078417/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
f = pd.read_csv('../input/time-series-forecasting-with-yahoo-stock-price/yahoo_stock.csv')
f.head() | code |
73078417/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pylab import plt
from statsmodels.tsa import stattools
import numpy as np
grid = np.linspace(0, 720, 500)
noise = np.random.rand(500)
result_curve = noise
acf_result = stattools.acf(result_curve)
grid = np.linspace(0, 100, 1000)
sin5 = np.sin(grid)
result_curve = sin5
grid = np.linspace(0, 100, 10... | code |
18154941/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_13 | [
"image_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test... | code |
18154941/cell_4 | [
"text_plain_output_1.png"
] | import torch
import torch
print('Make sure cudnn is enabled:', torch.backends.cudnn.enabled, torch.backends.cudnn.deterministic)
torch.backends.cudnn.deterministic = True
print('Make sure cudnn is enabled:', torch.backends.cudnn.enabled, torch.backends.cudnn.deterministic) | code |
18154941/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_20 | [
"image_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct = 0.1
bs = 64
size = 224
np.random.seed(42)
src = ImageList.from... | code |
18154941/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18154941/cell_18 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
fname = os.path.join(data_path, train_la... | code |
18154941/cell_32 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_28 | [
"image_output_2.png",
"image_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
18154941/cell_10 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test... | code |
18154941/cell_27 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
tfms = get_transforms(do_flip=True, flip_vert=True, max_warp=0)
data_path = '../input/aptos2019-blindness-detection'
train_label_file = 'train.csv'
train_images_folder = 'train_images'
test_label_file = 'test.csv'
test_images_folder = 'test_images'
image_suffix = '.png'
split_pct ... | code |
128008350/cell_3 | [
"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)
stimulus = pd.read_excel('/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Description.xlsx')
stimulus['Target Emotion'] = stimulus['Target Emotion'].str.title()
stimulus.info()
stimulus.head() | code |
128008350/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
stimulus=pd.read_excel("/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Description.xlsx")
stimulus["Target Emotion"]=stimulus["Target Emotion"].str.title()
stimulus.info()
stimulus.head()
ecg_data = pd.read... | code |
33098890/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-de... | code |
33098890/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-demand/%s.csv' % name)
df['_data'] = name
dfs[name] = df
df = dfs['train'].append(dfs['test'])
df.columns = map(str.lower, df.col... | code |
33098890/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-de... | code |
33098890/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))
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import mat... | code |
33098890/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-demand/%s.csv' % name)
df['_data'] = name
dfs[name] = df
df = dfs['train'].append(dfs['test'])
df.columns = map(str.lower, df.col... | code |
33098890/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-demand/%s.csv' % name)
df['_data'] = name
dfs[name] = df
df = dfs['train'].append(dfs['test'])
df.columns = map(str.lower, df.col... | code |
33098890/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-de... | code |
33098890/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-de... | code |
33098890/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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)
dfs = {}
for name in ['train', 'test']:
df = pd.read_csv('/kaggle/input/bike-sharing-de... | code |
2022076/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.... | code |
2022076/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import hamming_loss
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].... | code |
2022076/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df.head() | code |
2022076/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import fbeta_score
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].v... | code |
2022076/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -... | code |
2022076/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1... | code |
2022076/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_m... | code |
2022076/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, [... | code |
2022076/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:]... | code |
2022076/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].va... | code |
2022076/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:... | code |
2026938/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import nu... | code |
2026938/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import pandas as pd
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/te... | code |
2026938/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import pandas as pd
import time
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv... | code |
2026938/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t') | code |
2026938/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import nu... | code |
2026938/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | Time_0 = time.time()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.sparse import csr_matrix, hstack
import time
import re
import math
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, LabelBinarizer
from sklearn.cross_validation impor... | code |
2026938/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import numpy as np
import pandas as pd
import time
train = pd.read_csv('../input/train.tsv', sep='\t'... | code |
2026938/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import Ridge
ridge_model = Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None) | code |
2026938/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import nu... | code |
2026938/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import pandas as pd
import time
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv... | code |
1010539/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_hdf('../input/train.h5')
def myticks(x, pos):
exponent = abs(int(np.log10(np.abs(x))))
return exponent
def plot_exp(data, title):
fig, ax =plt.subplots(figsize = (12, 8))
ax.plot(data.t16_exp, data.timestamp)
... | code |
1010539/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_hdf('../input/train.h5')
def myticks(x, pos):
exponent = abs(int(np.log10(np.abs(x))))
return exponent
def plot_exp(data, title):
fig, ax =plt.subplots(figsize = (12, 8))
ax.plot(data.t16_exp, data.timestamp)
... | code |
1010539/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_hdf('../input/train.h5')
def myticks(x, pos):
exponent = abs(int(np.log10(np.abs(x))))
return exponent
def plot_exp(data, title):
fig, ax =plt.subplots(figsize = (12, 8))
ax.plot(data.t16_exp, data.timestamp)
... | code |
1010539/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_hdf('../input/train.h5')
def myticks(x, pos):
exponent = abs(int(np.log10(np.abs(x))))
return exponent
def plot_exp(data, title):
fig, ax =plt.subplots(figsize = (12, 8))
ax.plot(data.t16_exp, data.timestamp)
... | code |
1010539/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_hdf('../input/train.h5') | code |
50239241/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.age, df.trestbps)
df.corr() | code |
50239241/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target) | code |
50239241/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target) | code |
50239241/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.head() | code |
50239241/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.age, df.trestbps)
df.corr()
corr_matrix = df.corr()
plt.figure(figsize=(15, 10))
sns.heatmap(co... | code |
50239241/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape | code |
50239241/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
df['target'].value_counts() | code |
50239241/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.age, df.trestbps)
plt.figure(figsize=(10, 6))
plt.scatter(df.trestbps[df.target == 1], df.age[df.target == 1])
plt.scat... | code |
50239241/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.age, df.trestbps) | code |
50239241/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.cp, df.target).plot(kind='bar', rot=0, xlabel='Chest Pain', ylabel='Frequency', title='Frequency Graph between the Chest... | code |
50239241/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.sex, df.target).plot(kind='bar', rot=0, ylabel='Frequency', xlabel='Sex', title='Frequency graph between the Sex and Target', colormap='tab20c')
plt.le... | code |
50239241/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.describe() | code |
34123364/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
print(laureates_data[laureates_data['Laureate Type'] != 'Individual']['Laureate Type']) | code |
34123364/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
print(laureates_data.columns)
print(laureates_data[laureates_data.isnull().any(axis=1)]) | code |
34123364/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
print(laureates_data[laureates_data['Laureate Type'] != 'Individual']['Category'].unique()) | code |
34123364/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
print(laureates_data.head())
print(laureates_data.dtypes) | code |
34123364/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
print('The number of entries: %d \n\n' % laureates_data[laureates_data['Full Name'].str.contains('Marie Curie')].shape[0])
print(laureates_data[laureates_data['Full Name'].str.contains('Marie Curie')]) | code |
34123364/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
laureates_data = pd.read_csv('/kaggle/input/nobel-laureates/archive.csv')
name_counts = laureates_data['Full Name'].value_counts()
multi_name = list(name_counts[name_counts > 1].index)
for name in multi_name:
temp = laureates_data[laureates_data['Full Name'] == name].Year.... | code |
16132425/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
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 seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import tr... | code |
16132425/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
16132425/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
16132425/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_... | code |
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