path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90108947/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pylab as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (... | code |
90108947/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
df.info() | code |
90108947/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas... | code |
90108947/cell_20 | [
"image_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas... | code |
90108947/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
data_plot = pd.read_csv(data, sep=',', parse_dates=['Date'], in... | code |
90108947/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-h... | code |
90108947/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 |
90108947/cell_7 | [
"text_plain_output_1.png"
] | import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
df.shape
new_df = df.filter(['Close'])
dataset = new_df.values
training_data_len ... | code |
90108947/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
df.shape
new_df = df.filter(['Clo... | code |
90108947/cell_3 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df | code |
90108947/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
data_plot = pd.read_csv(data, sep=',', parse_dates=['Date'], in... | code |
90108947/cell_14 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas... | code |
90108947/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pylab as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (... | code |
90108947/cell_12 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas... | code |
90108947/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '/kaggle/input/110-1-ntut-dl-app-hw3/IXIC.csv'
df = pd.read_csv(data)
df
df.shape | code |
17115461/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../input/seg_train/seg_train/'
test_dir = '../input/seg_test/seg_test/'
pri... | code |
17115461/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../inpu... | code |
17115461/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Dropout, Flatten, Dense, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import tensorflow as tf
import os
def list_files(startpath):
for root, dirs, files in os.walk(start... | code |
17115461/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
list_files('../input') | code |
17115461/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Dropout, Flatten, Dense, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import os
import tensorflow as tf
import os
def list_files(startpath):
for r... | code |
17115461/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_height, 3), include_top=False, weights='imagenet')
for layer in vgg16.layers:
layer.trainable = False
vgg16.summary() | code |
17115461/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Dropout, Flatten, Dense, BatchNormalization
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_height, 3), include_top=False, weights='imagenet')
for laye... | code |
17115461/cell_5 | [
"text_plain_output_1.png"
] | import os
import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../input/seg_train/seg_train/'
print(os.listdir(train_dir)) | code |
330287/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
330287/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
330287/cell_7 | [
"image_output_1.png"
] | import brewer2mpl
import matplotlib.pyplot as plt
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
font = {'family': 'sans-serif', 'color': 'teal', 'weight': 'bold', 'size': 18}
plt.rc('font', family='serif')
plt.rc('font', size=16)
plt.rc('font', weight='bold')
plt.style.use('seaborn-dark-palette')
pri... | code |
330287/cell_8 | [
"text_html_output_1.png"
] | from matplotlib import rcParams
import brewer2mpl
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/p... | code |
330287/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
74049529/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 |
74049529/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
date1str = str(input('Enter date(yyyy-mm-dd): '))
date1 = datetime.strptime(date1str, '%Y-%m-%d')
date1after = date1 + pd.Timedelta(days=1)
print('Date after ', date1, ' is ', date1after)
date1bef... | code |
88093705/cell_23 | [
"text_plain_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_20 | [
"text_plain_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_50 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_4.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_html_output_2.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.... | from IPython.core.display import HTML
from IPython.display import Markdown
from scipy.stats import norm, skew, kurtosis
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import seaborn as sns
"""Personnalisation... | code |
88093705/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 |
88093705/cell_32 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_15 | [
"text_html_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_38 | [
"text_plain_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_46 | [
"text_html_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
88093705/cell_14 | [
"text_html_output_1.png"
] | from IPython.display import Markdown
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
"""Personnalisation de la visualisation"""
plt.style.use('bmh')
sns.set_style({'axes.grid': False})
'On peut utiliser le gras , souli... | code |
326100/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures[global_temperatures.index.year > 2000]['LandAverageTemperature'].plot(figsize=(13, 7)) | code |
326100/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)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperature'].mean().plot(figsize=(13, 7)) | code |
326100/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from matplotlib import pyplot as plt
import seaborn as sbn | code |
326100/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperatureUncertainty'].mean().plot(figsi... | code |
326100/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
global_temperatures = pd.read_csv('../input/GlobalTemperatures.csv', infer_datetime_format=True, index_col='dt', parse_dates=['dt'])
print(global_temperatures.info()) | code |
73074503/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape | code |
73074503/cell_25 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
o... | code |
73074503/cell_33 | [
"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"
] | X_train.head() | code |
73074503/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
y... | code |
73074503/cell_39 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
from sklearn.ensemble import BaggingRegressor
regr = BaggingRegressor(base_estimator=XGBRegressor(), n_estimators=10, random_state=0).fit(X_train, y_train)
preds_valid = regr.predict(X_valid)... | code |
73074503/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
y.dtype | code |
73074503/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
73074503/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
p... | code |
73074503/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from xgboost import XGBRegressor
params_xgb = {'lambda': 0.7044156083795233, 'alpha': 9.681476940192473, 'colsample_bytree': 0.3, 'subsample': 0.8, 'learning_rate': 0.015, 'max_depth': 3, 'min_child_weight': 235, 'random_state': 48, 'n_estimators': 30000}
XGBRegressor_model = XGBRegressor(**params_xgb)
'XGBRegressor_m... | code |
73074503/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import make_pipeline
from sklearn.pre... | code |
73074503/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
o... | code |
73074503/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
o... | code |
73074503/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts() | code |
73074503/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
o... | code |
73074503/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.dtypes.value_counts()
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
1010157/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.info() | code |
1010157/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df['PREVAILING_WAGE'].describe() | code |
1010157/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum()
df[['EMPLOYER_NAME', 'PREVAILING_WAGE']].groupby('EMPLOYER_NAME', as_index=False).mean().sort_values(by='PREVAILING_WAGE', ascending=False).head(20) | code |
1010157/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.describe(include=['O']) | code |
1010157/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum()
df.EMPLOYER_NAME.value_counts().head(20).plot(kind='bar') | code |
1010157/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.head() | code |
1010157/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum()
df.YEAR.value_counts().plot(kind='bar') | code |
1010157/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum()
df.WORKSITE.value_counts().head(20).plot(kind='bar') | code |
1010157/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum() | code |
1010157/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/h1b_kaggle.csv')
df.isnull().sum()
df.FULL_TIME_POSITION.value_counts().plot(kind='bar') | code |
128019012/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
price = [46000, 56000, 60000, 54000, 70000, 4500000]
year = [2018, 2019, 2020, 2021, 2022, 2023]
plt.... | code |
128019012/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
plt.plot(year, price) | code |
128019012/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
price = [46000, 56000, 60000, 54000, 70000, 4500000]
year = [2018, 2019, 2020, 2021, 2022, 2023]
pri... | code |
128019012/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
plt.plot(cricketer['index'], cricketer['RG Sharma']) | code |
128019012/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
price = [46000, 56000, 60000, 54000, 70000, 4500000]
year = [2018, 2019, 2020, 2021, 2022, 2023]
pri... | code |
128019012/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
plt.plot(cricketer['index'], cricketer['RG Sharma'])
plt.plot(cricketer['index'], cricketer['V Kohli'... | code |
128019012/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
plt.plot(cricketer['index'], cricketer['RG Sharma'])
plt.plot(cricketer['index'], cricketer['V Kohli'... | code |
128019012/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
price = [46000, 56000, 60000, 54000, 70000, 4500000]
year = [2018, 2019, 2020, 2021, 2022, 2023]
pri... | code |
128019012/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
a = pd.read_csv('/kaggle/input/batter/batter.csv')
a | code |
128019012/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
price = [46000, 56000, 60000, 54000, 70000, 4500000]
year = [2018, 2019, 2020, 2021, 2022, 2023]
pri... | code |
128019012/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
plt.plot(cricketer['index'], cricketer['RG Sharma'], color='Green', linestyle='dashed', linewidth=3, ... | code |
128019012/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
price = [46000, 56000, 60000, 54000, 70000]
year = [2018, 2019, 2020, 2021, 2022]
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer
plt.plot(cricketer['index'], cricketer['RG Sharma'], color='Green', linestyle='dashed', linewidth=3, ... | code |
128019012/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
cricketer = pd.read_csv('/kaggle/input/sharma-kohli/sharma-kohli.csv')
cricketer | code |
18121674/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from math import... | code |
18121674/cell_8 | [
"text_plain_output_1.png"
] | from math import exp
from random import randrange
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
di... | code |
18121674/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
diabetes_df = pd.read_csv('../input/diabetes.csv')
diabetes_df = diabetes_df.values
diabetes_df | code |
18121674/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
logistic_model = LogisticRegression()
logistic_model.fit(X_train, y_train)
predicted = logistic_model.predict(X_test)
lr_accur... | code |
333798/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='... | code |
333798/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys() | code |
333798/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_data['tweets']]
fanboy_ha... | code |
333798/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
from matplotlib import *
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
333798/cell_11 | [
"text_plain_output_1.png"
] | import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_da... | code |
333798/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy... | code |
333798/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split()... | code |
333798/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='... | code |
333798/cell_12 | [
"text_plain_output_1.png"
] | import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_da... | code |
128044361/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/men-born-in-1960-from-7-regions-in-korea-2005-2009/Data.csv', sep=';')
incplot = data.income.hist(grid=False) | code |
128044361/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/men-born-in-1960-from-7-regions-in-korea-2005-2009/Data.csv', sep=';')
data.head() | code |
128044361/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/men-born-in-1960-from-7-regions-in-korea-2005-2009/Data.csv', sep=';')
data.describe() | code |
128015173/cell_42 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_21 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
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