path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34135429/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.head(5) | code |
34135429/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
34135429/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as p... | code |
17109357/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.head() | code |
17109357/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns
data.Species.unique() | code |
17109357/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17109357/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #drawing library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns
data.Species.unique()
setosa = data[data.Species == 'Iris-setosa']
versicolor = data[data.Species == 'Iris-versicolor']
virginica = data[data.... | code |
17109357/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.info() | code |
17109357/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns | code |
130010265/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
cor... | code |
130010265/cell_9 | [
"image_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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
plt.boxplot(df['bmi'])
plt.xlabel('BMI')
plt.title('Box Plot of BMI')
plt.show() | code |
130010265/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.describe() | code |
130010265/cell_2 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble i... | code |
130010265/cell_11 | [
"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)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
plt.scatter(df['blood_glucose_level'], df['HbA1c_level'])
plt.xlabel('Blood... | code |
130010265/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.info() | code |
130010265/cell_8 | [
"image_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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
plt.hist(df['age'], bins=10, edgecolor='k')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Histogram of Age')
plt.... | code |
130010265/cell_15 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes... | code |
130010265/cell_16 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes... | code |
130010265/cell_10 | [
"application_vnd.jupyter.stderr_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('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
plt.bar(gender_counts.index, gender_counts.values)
plt.xlabel('Gender')
plt.... | code |
130010265/cell_12 | [
"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)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
plt.pie(diabetes_counts.val... | code |
130010265/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
16147703/cell_9 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64... | code |
16147703/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
train.tail() | code |
16147703/cell_2 | [
"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/train.csv')
test = pd.read_csv('../input/test_id.csv')
print(train.shape)
print(test.shape) | code |
16147703/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = t... | code |
16147703/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import os
print(os.listdir('../input')) | code |
16147703/cell_7 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64... | code |
16147703/cell_8 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64... | code |
16147703/cell_3 | [
"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/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
train.head() | code |
16147703/cell_10 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64... | code |
16147703/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = t... | code |
16147703/cell_5 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64... | code |
122255317/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.show() | code |
122255317/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
fig, ax = plt.subplots(figsize=(10, 10))
plt.show() | code |
122255317/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122255317/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
fig, ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='An Example Axes', ylabel='Y-Axis', xlabel='X-Axis')
plt.show() | code |
122255317/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
333413/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('..... | code |
333413/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
gatrain.head(3) | code |
333413/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
import xgboost as xgb | code |
333413/cell_11 | [
"text_html_output_1.png"
] | params = {'objective': 'multi:softprob', 'num_class': 12, 'booster': 'gbtree', 'max_depth': 8, 'eval_metric': 'mlogloss', 'eta': 0.02, 'silent': 1, 'alpha': 3} | code |
333413/cell_15 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('..... | code |
333413/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
phone.head(3) | code |
333413/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('..... | code |
333413/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
dup = phone.groupby('device_id').size()
d... | code |
333413/cell_12 | [
"text_html_output_1.png"
] | def encode_cat(Xtrain, Xtest):
model_age = Xtrain.groupby(['model'])['age'].agg('mean')
brand_age = Xtrain.groupby(['brand'])['age'].agg('mean')
Xtest['model_age'] = Xtest['model'].map(model_age)
Xtrain['model_age'] = Xtrain['model'].map(model_age)
Xtest['brand_age'] = Xtest['brand'].map(brand_age)
... | code |
333413/cell_5 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_770.png",
"text_plain_output_743.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_640.png",
"text_plain_output_822.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_... | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape | code |
105204911/cell_3 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip uninstall -q -y transformers | code |
105204911/cell_10 | [
"text_plain_output_1.png"
] | from torch import nn
from transformers import AutoModel
import torch
import torch
from torch import nn
from transformers import AutoModel
@torch.no_grad()
def turn_off_dropout(module: nn.Module) -> None:
if isinstance(module, nn.Dropout):
module.p = 0.0
model_path = 'distilbert-base-uncased'
model = Auto... | code |
50222260/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
classifier.score(x_test, y_test)
y_predicted = classifier.predict(x_test)
from sklearn.metrics ... | code |
50222260/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sm
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
from sklearn.ensemble import RandomForestClassi... | code |
50222260/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
classifier.score(x_test, y_test) | code |
50222260/cell_8 | [
"image_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
for i in range(5):
plt.matshow(digits.images[i]) | code |
50222260/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train) | code |
50222260/cell_10 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
x = pd.DataFrame(digits.data)
x | code |
50222260/cell_12 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
x = pd.DataFrame(digits.data)
x
y = pd.DataFrame(digits.target)
y | code |
106202736/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import numpy as np
import cv2
from numpy import save
image_arr_new = np.load('../input/imagearray201/image_array_20_1.npy')
image_array = []
for i in image_arr_new:
image_array.append(cv2.resize(i, (227, 227)))
image_array = np.array(image_array)
image_array.shape | code |
121154614/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/inpu... | code |
121154614/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels.api as sm
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegres... | code |
121154614/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/inpu... | code |
121154614/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/inpu... | code |
106208686/cell_13 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
import pandas
import tensorflow as tf
import tensorflow as t... | code |
106208686/cell_4 | [
"text_plain_output_1.png"
] | pip install keras-tcn | code |
106208686/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
print('len train ', len(data_train))
print('len test ', len(data_test))
O_data_test = data_test | code |
106208686/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import keras
from keras.layers import Dense
import os
import pandas
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106208686/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras_tuner impo... | code |
106208686/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
import pandas
import tensorflow as tf
import tensorflow as t... | code |
106208686/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
impo... | code |
106208686/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr =... | code |
106208686/cell_10 | [
"text_plain_output_1.png"
] | import pandas
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
print(data.head())
print(len(data))
data = list(data['value'])[:10200]
print(len(data)) | code |
106208686/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr =... | code |
16167156/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
data_lokasi.info() | code |
16167156/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
profil_karyawan.info() | code |
16167156/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
print(data_lokasi.head())
print('ukuran data: ' + str(data_lokasi.shape)... | code |
16167156/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
print(profil_karyawan.head())
print('ukuran data: ' + str(profil_karyawa... | code |
16130176/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38... | code |
16130176/cell_9 | [
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_inde... | code |
16130176/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16130176/cell_11 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_inde... | code |
16130176/cell_8 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_inde... | code |
16130176/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38... | code |
16130176/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38... | code |
16130176/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv') | code |
16151986/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import roc_auc_score
from sklearn.mixture import GaussianMixture
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-... | code |
16151986/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols =... | code |
73074157/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
plt.xlabel('Population in city in 10,000s')
plt.ylabel('Profit in £10,000s')
plt.title('Relationship between city si... | code |
73074157/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
df.info() | code |
73074157/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df | code |
73074157/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_t... | code |
73074157/cell_37 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_t... | code |
73074157/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_t... | code |
17120135/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,1... | code |
17120135/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
plt.figure(figsize=(20, 10))
sns.barplot(x=df_loc, y=df_loc.index)
plt.title('Top 20 locations with highest number of Restaurants.')
plt.xlabel('Cou... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.