path
stringlengths 13
17
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sequencelengths 1
873
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
74046505/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float64']]
numeric_cols
df.brand.unique()
df.bike_name.unique()
df.city.nunique()
df_missing = df.isna().sum()
sns.heatmap(data=df.corr(), annot=True) | code |
74046505/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float64']]
numeric_cols
df.brand.unique()
df.bike_name.unique()
df_unknown = df[df['bike_name'] == 'unknown']
df_unknown | code |
74046505/cell_27 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder
numerical_pipeline = Pipeline([('scaling', MinMaxScaler())])
categoric_second_pipeline = Pipeline([('label', OneHotEncoder(sparse=False))])
preprocessing = ColumnTransformer([('numeric', numerical_pipeline, ['kms_driven', 'age', 'power']), ('cat_second', categoric_second_pipeline, ['owner', 'bike_name', 'city', 'brand'])])
pipeline = Pipeline([('algo', preprocessing), ('model', RandomForestRegressor(random_state=42))])
pipeline.fit(X_train, y_train)
pipeline.get_params()
parameter = {'model__n_estimators': [100, 200, 350, 500], 'model__min_samples_leaf': [2, 10, 30]}
model = GridSearchCV(pipeline, parameter, cv=3, n_jobs=-1, verbose=1)
model.fit(X_train, y_train) | code |
74046505/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float64']]
numeric_cols
df.brand.unique()
df.bike_name.unique()
df.city.nunique()
df_missing = df.isna().sum()
print(df_missing[df_missing > 0]) | code |
74046505/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols | code |
18150474/cell_4 | [
"text_plain_output_1.png"
] | import sys
!conda install --yes --prefix {sys.prefix} -c rdkit rdkit | code |
18150474/cell_11 | [
"text_plain_output_1.png"
] | import os
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fname: str) -> str:
mol = next(pybel.readfile('xyz', fname))
smi = mol.write(format='smi')
return smi.split()[0].strip()
smiles = [xyz_to_smiles(file_dir + i) for i in tqdm(mol_name)] | code |
18150474/cell_3 | [
"text_html_output_1.png"
] | !conda install openbabel -c openbabel -y | code |
18150474/cell_14 | [
"text_plain_output_1.png"
] | from mol2vec.features import mol2alt_sentence
from rdkit import Chem
import os
import pandas as pd
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fname: str) -> str:
mol = next(pybel.readfile('xyz', fname))
smi = mol.write(format='smi')
return smi.split()[0].strip()
smiles = [xyz_to_smiles(file_dir + i) for i in tqdm(mol_name)]
df_smiles = pd.DataFrame({'molecule_name': mol_name, 'smiles': smiles})
sentence = mol2alt_sentence(Chem.MolFromSmiles(df_smiles.smiles[33]), 1)
print('SMILE:', df_smiles.smiles[33])
print(sentence) | code |
18150474/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fname: str) -> str:
mol = next(pybel.readfile('xyz', fname))
smi = mol.write(format='smi')
return smi.split()[0].strip()
smiles = [xyz_to_smiles(file_dir + i) for i in tqdm(mol_name)]
df_smiles = pd.DataFrame({'molecule_name': mol_name, 'smiles': smiles})
df_smiles.head(11) | code |
18150474/cell_5 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/samoturk/mol2vec | code |
106196488/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns | code |
106196488/cell_2 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
106196488/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show(1, vertical=True) | code |
106196488/cell_3 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark | code |
106196488/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show() | code |
1009103/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(montage2d(fossil_data), cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.hist(fossil_data.ravel(), 20)
ax2.set_title('Overall Histogram') | code |
1009103/cell_3 | [
"text_plain_output_1.png"
] | from skimage.io import imread
import numpy as np # linear algebra
fossil_path = '../input/Gut-PhilElvCropped.tif'
fossil_data_rgb = imread(fossil_path)
fossil_data = np.mean(fossil_data_rgb, -1)
print('Loading Fossil Data sized {}'.format(fossil_data.shape)) | code |
1009103/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | skip_slices = 30
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 5))
ax1.imshow(montage2d(fossil_data[skip_slices:-skip_slices]), cmap='bone')
ax1.set_title('Axial Slices')
ax2.imshow(montage2d(fossil_data.transpose(1, 2, 0)[skip_slices:-skip_slices]), cmap='bone')
ax2.set_title('Saggital Slices')
ax3.imshow(montage2d(fossil_data.transpose(2, 0, 1)[skip_slices:-skip_slices]), cmap='bone')
ax3.set_title('Coronal Slices') | code |
16157745/cell_6 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_13.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_16.png",
"application_vnd.jupyter.stderr_output_15.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_12.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
16157745/cell_32 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from sklearn.metrics import f1_score, accuracy_score
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y_train, y_test = (np.matrix(y_train_df).T, np.matrix(y_test_df).T)
def get_w_hat(X, t):
xt_x_minus_one = np.linalg.inv(np.dot(X.T, X))
xt_t = np.dot(X.T, t)
w_hat = np.dot(xt_x_minus_one, xt_t)
return w_hat
def get_sigma_hat(w, X, t, N):
X_w = np.dot(X, w)
t_minus_X_w = t - X_w
sigma_hat = 1.0 / N * np.dot(t_minus_X_w.T, t_minus_X_w)
return np.float(sigma_hat)
def get_Sigma_w(sigma_square, X, S):
return np.linalg.inv(1.0 / sigma_square * np.dot(X.T, X) + np.linalg.inv(S))
def get_mean_w(sigma_square, Sigma, X, t):
return 1.0 / sigma_square * np.dot(np.dot(Sigma, X.T), t)
class BayesianLinearRegressor:
def __init__(self, n=1, sigma_unif=1.0):
self.n = n
self.sigma_unif = sigma_unif
def lrfit(self, X_, t):
X = get_X_powered(X_, self.n)
X = get_X_w_bias(X)
_, S = generate_prior(X.shape[1], sigma_unif=self.sigma_unif)
w_hat = get_w_hat(X, t)
sigma_square = get_sigma_hat(w_hat, X, t, X.shape[0])
Sigma = get_Sigma_w(sigma_square, X, S)
mean_w = get_mean_w(sigma_square, Sigma, X, t)
self.mean_w, self.sigma_square, self.Sigma = (mean_w, sigma_square, Sigma)
def predict(self, X):
X_pow = get_X(X, self.n)
return np.dot(X_pow, self.mean_w)
def get_X_powered(X, n):
"""
Return the X matrix concat with all the samples powered to 2..n
Args:
X (numpy.matrix): Matrix (N, D)
- N : number of rows
- D : number of features
n (str): The power of the matrix.
Returns:
X_result: Matrix X with all the n powers
"""
if n <= 1:
return X
X_result = deepcopy(X)
X_powered = deepcopy(X)
for i in range(2, n + 1):
X_powered = np.multiply(X_powered, X)
X_result = np.concatenate((X_result, X_powered), axis=1)
return X_result
def get_X_w_bias(X):
"""
Return The X matrix with the bias term
Args:
X (numpy.matrix): Matrix X (N, D)
Returns:
X_result (numpy.matrix): Matrix X (N, D + 1) with bias term put on the right column
"""
X_result = np.concatenate((X, np.matrix(np.ones(X.shape[0])).T), axis=1)
return X_result
def get_X(X, n=1):
"""
Put together the 2 functions above since they are often used together
"""
return get_X_w_bias(get_X_powered(X, n))
def generate_prior(features, sigma_unif=3.0):
S = np.matrix(np.diag(sigma_unif * np.ones(features)))
return (np.matrix(np.zeros(features)), S)
def print_err(y_pred, y, total=False):
"""
Display the error of the Regression predictions
"""
err = y - y_pred
tot_err = np.sum(np.multiply(err, err))
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
@np.vectorize
def discretize(pred, threshold=0.5):
"""
The decorator vectorize allows to apply the function to each element
"""
return 1 if pred > threshold else 0
blr = BayesianLinearRegressor(n=3)
blr.lrfit(X_train, y_train)
y_test_und = blr.predict(X_test)
def print_metrics(y_test, y_pred):
"""
Utility function to display the most important metrics
"""
y_pred = discretize(y_test_und, 0.5)
print_metrics(y_pred, y_test) | code |
16157745/cell_28 | [
"text_plain_output_1.png"
] | from copy import deepcopy
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y_train, y_test = (np.matrix(y_train_df).T, np.matrix(y_test_df).T)
def get_w_hat(X, t):
xt_x_minus_one = np.linalg.inv(np.dot(X.T, X))
xt_t = np.dot(X.T, t)
w_hat = np.dot(xt_x_minus_one, xt_t)
return w_hat
def get_sigma_hat(w, X, t, N):
X_w = np.dot(X, w)
t_minus_X_w = t - X_w
sigma_hat = 1.0 / N * np.dot(t_minus_X_w.T, t_minus_X_w)
return np.float(sigma_hat)
def get_Sigma_w(sigma_square, X, S):
return np.linalg.inv(1.0 / sigma_square * np.dot(X.T, X) + np.linalg.inv(S))
def get_mean_w(sigma_square, Sigma, X, t):
return 1.0 / sigma_square * np.dot(np.dot(Sigma, X.T), t)
class BayesianLinearRegressor:
def __init__(self, n=1, sigma_unif=1.0):
self.n = n
self.sigma_unif = sigma_unif
def lrfit(self, X_, t):
X = get_X_powered(X_, self.n)
X = get_X_w_bias(X)
_, S = generate_prior(X.shape[1], sigma_unif=self.sigma_unif)
w_hat = get_w_hat(X, t)
sigma_square = get_sigma_hat(w_hat, X, t, X.shape[0])
Sigma = get_Sigma_w(sigma_square, X, S)
mean_w = get_mean_w(sigma_square, Sigma, X, t)
self.mean_w, self.sigma_square, self.Sigma = (mean_w, sigma_square, Sigma)
def predict(self, X):
X_pow = get_X(X, self.n)
return np.dot(X_pow, self.mean_w)
def get_X_powered(X, n):
"""
Return the X matrix concat with all the samples powered to 2..n
Args:
X (numpy.matrix): Matrix (N, D)
- N : number of rows
- D : number of features
n (str): The power of the matrix.
Returns:
X_result: Matrix X with all the n powers
"""
if n <= 1:
return X
X_result = deepcopy(X)
X_powered = deepcopy(X)
for i in range(2, n + 1):
X_powered = np.multiply(X_powered, X)
X_result = np.concatenate((X_result, X_powered), axis=1)
return X_result
def get_X_w_bias(X):
"""
Return The X matrix with the bias term
Args:
X (numpy.matrix): Matrix X (N, D)
Returns:
X_result (numpy.matrix): Matrix X (N, D + 1) with bias term put on the right column
"""
X_result = np.concatenate((X, np.matrix(np.ones(X.shape[0])).T), axis=1)
return X_result
def get_X(X, n=1):
"""
Put together the 2 functions above since they are often used together
"""
return get_X_w_bias(get_X_powered(X, n))
def generate_prior(features, sigma_unif=3.0):
S = np.matrix(np.diag(sigma_unif * np.ones(features)))
return (np.matrix(np.zeros(features)), S)
def print_err(y_pred, y, total=False):
"""
Display the error of the Regression predictions
"""
err = y - y_pred
tot_err = np.sum(np.multiply(err, err))
blr = BayesianLinearRegressor(n=3)
blr.lrfit(X_train, y_train)
y_test_und = blr.predict(X_test)
posterior_variance = blr.Sigma
print('Posterior variance matrix with shape: ', posterior_variance.shape) | code |
16157745/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
fig, ax = plt.subplots(figsize=(13, 3))
# TR the count is computed automatically
g = sns.countplot(x='target', data=df)
plt.show()
fig, ax = plt.subplots(ncols=3, figsize=(20, 4))
sns.distplot(df['var_0'], ax=ax[0], color='red')
sns.distplot(df['var_1'], ax=ax[1], color='green')
sns.distplot(df['var_3'], ax=ax[2], color='blue')
plt.show() | code |
16157745/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
fig, ax = plt.subplots(figsize=(13, 3))
g = sns.countplot(x='target', data=df)
plt.show() | code |
16157745/cell_27 | [
"image_output_1.png"
] | from copy import deepcopy
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y_train, y_test = (np.matrix(y_train_df).T, np.matrix(y_test_df).T)
def get_w_hat(X, t):
xt_x_minus_one = np.linalg.inv(np.dot(X.T, X))
xt_t = np.dot(X.T, t)
w_hat = np.dot(xt_x_minus_one, xt_t)
return w_hat
def get_sigma_hat(w, X, t, N):
X_w = np.dot(X, w)
t_minus_X_w = t - X_w
sigma_hat = 1.0 / N * np.dot(t_minus_X_w.T, t_minus_X_w)
return np.float(sigma_hat)
def get_Sigma_w(sigma_square, X, S):
return np.linalg.inv(1.0 / sigma_square * np.dot(X.T, X) + np.linalg.inv(S))
def get_mean_w(sigma_square, Sigma, X, t):
return 1.0 / sigma_square * np.dot(np.dot(Sigma, X.T), t)
class BayesianLinearRegressor:
def __init__(self, n=1, sigma_unif=1.0):
self.n = n
self.sigma_unif = sigma_unif
def lrfit(self, X_, t):
X = get_X_powered(X_, self.n)
X = get_X_w_bias(X)
_, S = generate_prior(X.shape[1], sigma_unif=self.sigma_unif)
w_hat = get_w_hat(X, t)
sigma_square = get_sigma_hat(w_hat, X, t, X.shape[0])
Sigma = get_Sigma_w(sigma_square, X, S)
mean_w = get_mean_w(sigma_square, Sigma, X, t)
self.mean_w, self.sigma_square, self.Sigma = (mean_w, sigma_square, Sigma)
def predict(self, X):
X_pow = get_X(X, self.n)
return np.dot(X_pow, self.mean_w)
def get_X_powered(X, n):
"""
Return the X matrix concat with all the samples powered to 2..n
Args:
X (numpy.matrix): Matrix (N, D)
- N : number of rows
- D : number of features
n (str): The power of the matrix.
Returns:
X_result: Matrix X with all the n powers
"""
if n <= 1:
return X
X_result = deepcopy(X)
X_powered = deepcopy(X)
for i in range(2, n + 1):
X_powered = np.multiply(X_powered, X)
X_result = np.concatenate((X_result, X_powered), axis=1)
return X_result
def get_X_w_bias(X):
"""
Return The X matrix with the bias term
Args:
X (numpy.matrix): Matrix X (N, D)
Returns:
X_result (numpy.matrix): Matrix X (N, D + 1) with bias term put on the right column
"""
X_result = np.concatenate((X, np.matrix(np.ones(X.shape[0])).T), axis=1)
return X_result
def get_X(X, n=1):
"""
Put together the 2 functions above since they are often used together
"""
return get_X_w_bias(get_X_powered(X, n))
def generate_prior(features, sigma_unif=3.0):
S = np.matrix(np.diag(sigma_unif * np.ones(features)))
return (np.matrix(np.zeros(features)), S)
def print_err(y_pred, y, total=False):
"""
Display the error of the Regression predictions
"""
err = y - y_pred
tot_err = np.sum(np.multiply(err, err))
blr = BayesianLinearRegressor(n=3)
blr.lrfit(X_train, y_train)
y_test_und = blr.predict(X_test)
print_err(y_test_und, y_test, True) | code |
122258955/cell_2 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install pytorch-lightning | code |
122258955/cell_11 | [
"text_plain_output_1.png"
] | from torch import nn
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from torchvision.datasets import MNIST
import os
import os
import pytorch_lightning as L
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
import os
class TinyModel(torch.nn.Module):
def __init__(self):
super(TinyModel, self).__init__()
self.linear1 = torch.nn.Linear(100, 20)
self.activation1 = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(20, 5)
self.activation2 = torch.nn.ReLU()
self.linear3 = torch.nn.Linear(5, 2)
def forward(self, x):
x = self.linear1(x)
x = self.activation1(x)
x = self.linear2(x)
x = self.activation2(x)
x = self.linear3(x)
return x
class LitAutoEncoder(L.LightningModule):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
def forward(self, x):
embedding = self.encoder(x)
return embedding
def training_step(self, batch, batch_idx):
x, y = batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
self.log('val_loss', loss, prog_bar=True)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
return optimizer
dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
train, val = random_split(dataset, [55000, 5000])
train_dataloader = DataLoader(train, batch_size=64, num_workers=4)
val_dataloader = DataLoader(val, batch_size=64, num_workers=4)
autoencoder = LitAutoEncoder()
trainer = L.Trainer(val_check_interval=250, accelerator='gpu', devices=2, max_epochs=20, precision=16)
trainer.fit(autoencoder, train_dataloader, val_dataloader) | code |
89135962/cell_21 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
val_cutoff = train['time'].max() - dt.timedelta(hours=12)
X_val = train[train['time'] > val_cutoff].reset_index(drop=True)
print(X_val['time'].min(), X_val['time'].max())
print(X_val['time'].max() - X_val['time'].min()) | code |
89135962/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
train.groupby('roadway')['congestion'].agg('mean').plot.bar() | code |
89135962/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
df_sub = test[['row_id', 'roadway']].copy()
preds_test = train.groupby('roadway')['congestion'].median().rename('congestion').reset_index().round(0).astype(int)
df_sub = df_sub.merge(preds_test, on='roadway', how='left')
df_sub | code |
89135962/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train | code |
89135962/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import metrics
import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
val_cutoff = train['time'].max() - dt.timedelta(hours=12)
X_train = train[train['time'] <= val_cutoff].reset_index(drop=True)
X_val = train[train['time'] > val_cutoff].reset_index(drop=True)
preds = X_train.groupby('roadway')['congestion'].median().rename('y_pred').reset_index().round(0).astype(int)
df_preds = X_val.merge(preds, on='roadway', how='left')
mae = metrics.mean_absolute_error(df_preds['congestion'], df_preds['y_pred'])
print('MAE:', mae) | code |
89135962/cell_20 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
val_cutoff = train['time'].max() - dt.timedelta(hours=12)
X_train = train[train['time'] <= val_cutoff].reset_index(drop=True)
print(X_train['time'].min(), X_train['time'].max())
print(X_train['time'].max() - X_train['time'].min()) | code |
89135962/cell_29 | [
"text_plain_output_1.png"
] | !head submission.csv | code |
89135962/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
df_sub = test[['row_id', 'roadway']].copy()
preds_test = train.groupby('roadway')['congestion'].median().rename('congestion').reset_index().round(0).astype(int)
df_sub = df_sub.merge(preds_test, on='roadway', how='left')
df_sub
plt.hist(train['congestion'], density=True, alpha=0.5, bins=30, label='Observed')
plt.hist(df_sub['congestion'], density=True, alpha=0.5, bins=30, label='Predicted')
plt.legend() | code |
89135962/cell_2 | [
"text_plain_output_1.png"
] | PATH = Path('../input/tabular-playground-series-mar-2022')
!ls {PATH} | code |
89135962/cell_19 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
val_cutoff = train['time'].max() - dt.timedelta(hours=12)
print('val_cutoff:', val_cutoff) | code |
89135962/cell_7 | [
"image_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
train['congestion'].plot.hist() | code |
89135962/cell_18 | [
"image_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
print(test['time'].min(), test['time'].max())
print(test['time'].max() - test['time'].min()) | code |
89135962/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
train['y'].value_counts().sort_index() | code |
89135962/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
print(train['time'].min(), train['time'].max())
print(train['time'].max() - train['time'].min()) | code |
89135962/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0);
train['x'].value_counts().sort_index() | code |
89135962/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
plt.plot(df_roadway['congestion']) | code |
89135962/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return df_
train = pd.read_csv(PATH / 'train.csv')
train = preprocess(train)
test = pd.read_csv(PATH / 'test.csv')
test = preprocess(test)
train
df_roadway = train[train['roadway'] == 0].reset_index(drop=True)
ax = train.groupby('direction')['congestion'].mean().plot.bar()
ax.bar_label(ax.containers[0])
plt.xticks(rotation=0) | code |
128011626/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 24, 27, 29, 33, 43, 44, 47, 49, 51, 69, 75, 77]
bad_cols = df.columns[bad_data_indexes]
bad_cols | code |
128011626/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from functools import reduce
import operator
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128011626/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
for c in dfcolumns_indexes:
col_name = dfcolumns_indexes[c]
bad_data_indexes = [9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 24, 27, 29, 33, 43, 44, 47, 49, 51, 69, 75, 77]
bad_cols = df.columns[bad_data_indexes]
bad_cols
for col_name in bad_cols:
print(f'{col_name}: {df[col_name].nunique()}') | code |
128011626/cell_8 | [
"text_html_output_1.png"
] | import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 24, 27, 29, 33, 43, 44, 47, 49, 51, 69, 75, 77]
bad_cols = df.columns[bad_data_indexes]
bad_cols
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
bad_float_types = set(df.columns)
categorical_columns = ['Make', 'drive_wheels', 'Series', 'boost_type', 'back_suspension', 'country_of_origin', 'presence_of_intercooler', 'Generation', 'car_class', 'Body_type', 'rear_brakes', 'rating_name', 'emission_standards', 'front_suspension', 'steering_type', 'injection_type', 'transmission', 'fuel_grade', 'cylinder_layout', 'front_brakes', 'Modle', 'engine_type', 'overhead_camshaft']
cateforical_columns_check = ['engine_placement', 'Trim', 'number_of_seats', 'wheel_size_r14', 'front_rear_axle_load_kg', 'maximum_torque_n_m']
weird_columns = ['turnover_of_maximum_torque_rpm', 'range_km']
time_coumns = ['safety_assessment']
for c in time_coumns:
bad_float_types.discard(c)
for c in cateforical_columns_check:
bad_float_types.discard(c)
for c in categorical_columns:
bad_float_types.discard(c)
for c in weird_columns:
bad_float_types.discard(c)
def is_numeric(value):
try:
float(value)
return True
except ValueError:
return False
def cast_float_comma(value: str):
return value.replace(',', '.') if ',' in value else value
def cast_float_space(value: str):
return value.replace(' ', '') if ' ' in value else value
def cast_cylinder_bore_and_stroke_cycle_mm(value: str):
if value == 'nan':
return 'nan'
if value:
return 'x'.join([str(round(float(x.replace(',', '.')), 2)) for x in value.split('x')])
else:
return None
bad_comma_data = ['load_height_mm']
bad_space_data = ['clearance_mm']
bad_cylinder_bore_and_stroke_cycle_mm = ['cylinder_bore_and_stroke_cycle_mm']
bad_max_speed_km_per_h = ['max_speed_km_per_h']
bad_overhead_camshaft = ['overhead_camshaft']
bad_engine_hp_rpm = ['engine_hp_rpm']
bad_cargo_compartment_length_width_height_mm = ['cargo_compartment_length_width_height_mm']
for bad_col_name in bad_cargo_compartment_length_width_height_mm:
df[bad_col_name] = df[bad_col_name].astype(str)
prod_lambda = lambda x: str(reduce(operator.mul, (float(i) for i in x.split('x')), 1)) if x and len(x.split('x')) == 3 else 'nan'
df[bad_col_name] = df[bad_col_name].apply(lambda x: prod_lambda(x))
for bad_col_name in bad_engine_hp_rpm:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(lambda x: 'nan' if '-' in x else x)
for bad_col_name in bad_max_speed_km_per_h:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(lambda x: 'nan' if x == 'km/h' else x)
for bad_col_name in bad_cylinder_bore_and_stroke_cycle_mm:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_cylinder_bore_and_stroke_cycle_mm)
for bad_col_name in bad_space_data:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_float_space)
for bad_col_name in bad_float_types:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_float_comma)
non_float_ignore = set(['cylinder_bore_and_stroke_cycle_mm'])
for ii, bad_col_name in enumerate(bad_float_types):
if bad_col_name in non_float_ignore:
continue
df[bad_col_name] = df[bad_col_name].astype(str)
mask = df[bad_col_name].apply(is_numeric)
non_numeric_values = df.loc[~mask, bad_col_name]
if len(non_numeric_values) > 0:
print(bad_col_name, ii)
print(non_numeric_values)
break
df[bad_col_name] = df[bad_col_name].astype(float)
print('run without errors!') | code |
128011626/cell_10 | [
"text_plain_output_1.png"
] | import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 24, 27, 29, 33, 43, 44, 47, 49, 51, 69, 75, 77]
bad_cols = df.columns[bad_data_indexes]
bad_cols
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
bad_float_types = set(df.columns)
categorical_columns = ['Make', 'drive_wheels', 'Series', 'boost_type', 'back_suspension', 'country_of_origin', 'presence_of_intercooler', 'Generation', 'car_class', 'Body_type', 'rear_brakes', 'rating_name', 'emission_standards', 'front_suspension', 'steering_type', 'injection_type', 'transmission', 'fuel_grade', 'cylinder_layout', 'front_brakes', 'Modle', 'engine_type', 'overhead_camshaft']
cateforical_columns_check = ['engine_placement', 'Trim', 'number_of_seats', 'wheel_size_r14', 'front_rear_axle_load_kg', 'maximum_torque_n_m']
weird_columns = ['turnover_of_maximum_torque_rpm', 'range_km']
time_coumns = ['safety_assessment']
for c in time_coumns:
bad_float_types.discard(c)
for c in cateforical_columns_check:
bad_float_types.discard(c)
for c in categorical_columns:
bad_float_types.discard(c)
for c in weird_columns:
bad_float_types.discard(c)
def is_numeric(value):
try:
float(value)
return True
except ValueError:
return False
def cast_float_comma(value: str):
return value.replace(',', '.') if ',' in value else value
def cast_float_space(value: str):
return value.replace(' ', '') if ' ' in value else value
def cast_cylinder_bore_and_stroke_cycle_mm(value: str):
if value == 'nan':
return 'nan'
if value:
return 'x'.join([str(round(float(x.replace(',', '.')), 2)) for x in value.split('x')])
else:
return None
bad_comma_data = ['load_height_mm']
bad_space_data = ['clearance_mm']
bad_cylinder_bore_and_stroke_cycle_mm = ['cylinder_bore_and_stroke_cycle_mm']
bad_max_speed_km_per_h = ['max_speed_km_per_h']
bad_overhead_camshaft = ['overhead_camshaft']
bad_engine_hp_rpm = ['engine_hp_rpm']
bad_cargo_compartment_length_width_height_mm = ['cargo_compartment_length_width_height_mm']
for bad_col_name in bad_cargo_compartment_length_width_height_mm:
df[bad_col_name] = df[bad_col_name].astype(str)
prod_lambda = lambda x: str(reduce(operator.mul, (float(i) for i in x.split('x')), 1)) if x and len(x.split('x')) == 3 else 'nan'
df[bad_col_name] = df[bad_col_name].apply(lambda x: prod_lambda(x))
for bad_col_name in bad_engine_hp_rpm:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(lambda x: 'nan' if '-' in x else x)
for bad_col_name in bad_max_speed_km_per_h:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(lambda x: 'nan' if x == 'km/h' else x)
for bad_col_name in bad_cylinder_bore_and_stroke_cycle_mm:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_cylinder_bore_and_stroke_cycle_mm)
for bad_col_name in bad_space_data:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_float_space)
for bad_col_name in bad_float_types:
df[bad_col_name] = df[bad_col_name].astype(str)
df[bad_col_name] = df[bad_col_name].apply(cast_float_comma)
non_float_ignore = set(['cylinder_bore_and_stroke_cycle_mm'])
for ii, bad_col_name in enumerate(bad_float_types):
if bad_col_name in non_float_ignore:
continue
df[bad_col_name] = df[bad_col_name].astype(str)
mask = df[bad_col_name].apply(is_numeric)
non_numeric_values = df.loc[~mask, bad_col_name]
if len(non_numeric_values) > 0:
break
df[bad_col_name] = df[bad_col_name].astype(float)
df = df.rename(columns={'Modle': 'model', 'cargo_compartment_length_width_height_mm': 'cargo_compartment_volume_mm3'})
df = df.rename(columns={c: c.lower() for c in df.columns})
df.head() | code |
128011626/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
for c in dfcolumns_indexes:
col_name = dfcolumns_indexes[c]
print(f'{col_name}: {df[col_name].nunique()}') | code |
88088751/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Index', y='Weight', hue='Gender', kind='box', data=data) | code |
88088751/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.lmplot(x='Height', y='Weight', hue='Gender', data=data) | code |
88088751/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)
data = pd.read_csv('../input/bmidataset/bmi.csv')
data.head() | code |
88088751/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Gender', y='Weight', data=data) | code |
88088751/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from matplotlib import rcParams
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88088751/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.scatterplot(x='Height', y='Weight', hue='Gender', data=data) | code |
88088751/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.countplot(x='Index', data=data) | code |
88088751/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
plt.figure(figsize=(40, 16))
sns.barplot(x=data['Height'], y=data['Weight']) | code |
88088751/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.barplot(x='Index', y='Height', hue='Gender', data=data) | code |
88088751/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.barplot(x='Index', y='Weight', hue='Gender', data=data) | code |
88088751/cell_10 | [
"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)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Gender', y='Height', data=data) | code |
88088751/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Index', y='Height', hue='Gender', kind='box', data=data) | code |
16117222/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape | code |
16117222/cell_30 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):
train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)
train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)
# Гистограммы вычисляются с учетом длины вектора градиента
train_hist = np.zeros((len(train_img), 16))
for i in range(len(train_img)):
hist, borders = np.histogram(train_theta[i],
bins=16,
range=(0., 2. * np.pi),
weights=train_g[i])
train_hist[i] = hist
train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape
data_mean = np.mean(data_for_svd, axis=0)
data_for_svd -= data_mean
cov_matrix = np.dot(data_for_svd.T, data_for_svd) / data_for_svd.shape[0]
U, S, _ = np.linalg.svd(cov_matrix)
S_thr = 0.83
S_cumsum = 0
for i in range(S.shape[0]):
S_cumsum += S[i] / np.sum(S)
if S_cumsum >= S_thr:
n_comp = i + 1
break
data_reduced = np.dot(data_for_svd, U[:, :n_comp])
data_reduced.shape | code |
16117222/cell_33 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):
train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)
train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)
# Гистограммы вычисляются с учетом длины вектора градиента
train_hist = np.zeros((len(train_img), 16))
for i in range(len(train_img)):
hist, borders = np.histogram(train_theta[i],
bins=16,
range=(0., 2. * np.pi),
weights=train_g[i])
train_hist[i] = hist
train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]
train_hist.shape
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape
data_mean = np.mean(data_for_svd, axis=0)
data_for_svd -= data_mean
cov_matrix = np.dot(data_for_svd.T, data_for_svd) / data_for_svd.shape[0]
U, S, _ = np.linalg.svd(cov_matrix)
S_thr = 0.83
S_cumsum = 0
for i in range(S.shape[0]):
S_cumsum += S[i] / np.sum(S)
if S_cumsum >= S_thr:
n_comp = i + 1
break
data_reduced = np.dot(data_for_svd, U[:, :n_comp])
data_reduced.shape
train_data_svd = data_reduced[:42000]
test_data_svd = data_reduced[42000:]
(train_data_svd.shape, test_data_svd.shape)
train_data = np.hstack((train_hist, train_data_svd))
train_data.shape | code |
16117222/cell_6 | [
"image_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape | code |
16117222/cell_29 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):
train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)
train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)
# Гистограммы вычисляются с учетом длины вектора градиента
train_hist = np.zeros((len(train_img), 16))
for i in range(len(train_img)):
hist, borders = np.histogram(train_theta[i],
bins=16,
range=(0., 2. * np.pi),
weights=train_g[i])
train_hist[i] = hist
train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape
data_mean = np.mean(data_for_svd, axis=0)
data_for_svd -= data_mean
cov_matrix = np.dot(data_for_svd.T, data_for_svd) / data_for_svd.shape[0]
U, S, _ = np.linalg.svd(cov_matrix)
S_thr = 0.83
S_cumsum = 0
for i in range(S.shape[0]):
S_cumsum += S[i] / np.sum(S)
if S_cumsum >= S_thr:
n_comp = i + 1
print('n_comp:', n_comp, '\t', 'cumsum:', S_cumsum)
break | code |
16117222/cell_32 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):
train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)
train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)
# Гистограммы вычисляются с учетом длины вектора градиента
train_hist = np.zeros((len(train_img), 16))
for i in range(len(train_img)):
hist, borders = np.histogram(train_theta[i],
bins=16,
range=(0., 2. * np.pi),
weights=train_g[i])
train_hist[i] = hist
train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape
data_mean = np.mean(data_for_svd, axis=0)
data_for_svd -= data_mean
cov_matrix = np.dot(data_for_svd.T, data_for_svd) / data_for_svd.shape[0]
U, S, _ = np.linalg.svd(cov_matrix)
S_thr = 0.83
S_cumsum = 0
for i in range(S.shape[0]):
S_cumsum += S[i] / np.sum(S)
if S_cumsum >= S_thr:
n_comp = i + 1
break
data_reduced = np.dot(data_for_svd, U[:, :n_comp])
data_reduced.shape
train_data_svd = data_reduced[:42000]
test_data_svd = data_reduced[42000:]
(train_data_svd.shape, test_data_svd.shape) | code |
16117222/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot = fig.add_subplot(1, 7, i)
plt.imshow(img, cmap='gray')
subplot.set_title('%s' % train_label[i - 1]) | code |
16117222/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot = fig.add_subplot(1, 7, i)
plt.imshow(img, cmap='gray');
subplot.set_title('%s' % train_label[i - 1]);
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_g[:5], 1):
subplot = fig.add_subplot(1, 7, i)
plt.imshow(img, cmap='gray')
subplot.set_title('%s' % train_label[i - 1])
subplot = fig.add_subplot(3, 7, i)
plt.hist(train_theta[i - 1].flatten(), bins=16, weights=train_g[i - 1].flatten()) | code |
16117222/cell_24 | [
"image_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train.shape | code |
16117222/cell_22 | [
"image_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):
train_sobel_x[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=1, dy=0, ksize=3)
train_sobel_y[i] = cv2.Sobel(train_img[i], cv2.CV_64F, dx=0, dy=1, ksize=3)
# Гистограммы вычисляются с учетом длины вектора градиента
train_hist = np.zeros((len(train_img), 16))
for i in range(len(train_img)):
hist, borders = np.histogram(train_theta[i],
bins=16,
range=(0., 2. * np.pi),
weights=train_g[i])
train_hist[i] = hist
train_hist = train_hist / np.linalg.norm(train_hist, axis=1)[:, None]
train_hist.shape | code |
16117222/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot = fig.add_subplot(1, 7, i)
plt.imshow(img, cmap='gray');
subplot.set_title('%s' % train_label[i - 1]);
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_g[:5], 1):
subplot = fig.add_subplot(1, 7, i)
plt.imshow(img, cmap='gray');
subplot.set_title('%s' % train_label[i - 1]);
subplot = fig.add_subplot(3, 7, i)
plt.hist(train_theta[i - 1].flatten(),
bins=16, weights=train_g[i - 1].flatten())
h, w = train_img.shape[1:]
cX, cY = (int(w * 0.5), int(h * 0.5))
segments = [(0, w, 0, cY), (0, w, cY, h), (0, cX, 0, h), (cX, w, 0, h)]
fig = plt.figure(figsize=(16, 4))
for num, i in enumerate(segments, 1):
subplot = fig.add_subplot(1, 4, num)
plt.imshow(train_img[1, i[0]:i[1], i[2]:i[3]], cmap='gray') | code |
128011726/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('train_users_2.csv')
df1 | code |
128011726/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 |
18129647/cell_30 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
BATCH_SIZE = 100
IMG_SHAPE = 150
train_image_generator = ImageDataGenerator(rescale=1.0 / 255)
validation_image_generator = ImageDataGenerator(rescale=1.0 / 255)
train_data_gen = train_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=train_dir, shuffle=True, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary') | code |
18129647/cell_44 | [
"text_plain_output_1.png"
] | import logging
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(2, activation='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary() | code |
18129647/cell_50 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))
num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))
total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val
BATCH_SIZE = 100
IMG_SHAPE = 150
train_image_generator = ImageDataGenerator(rescale=1.0 / 255)
validation_image_generator = ImageDataGenerator(rescale=1.0 / 255)
train_data_gen = train_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=train_dir, shuffle=True, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary')
val_data_gen = validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=validation_dir, shuffle=False, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary')
# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
fig, axes = plt.subplots(1, 5, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
plt.tight_layout()
plt.show()
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(2, activation='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
EPOCHS = 100
history = model.fit_generator(train_data_gen, steps_per_epoch=int(np.ceil(total_train / float(BATCH_SIZE))), epochs=EPOCHS, validation_data=val_data_gen, validation_steps=int(np.ceil(total_val / float(BATCH_SIZE))))
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(EPOCHS)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.savefig('./foo.png')
plt.show() | code |
18129647/cell_16 | [
"text_plain_output_1.png"
] | zip_dir_base = os.path.dirname(zip_dir)
!find $zip_dir_base -type d -print | code |
18129647/cell_47 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import numpy as np
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))
num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))
total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val
BATCH_SIZE = 100
IMG_SHAPE = 150
train_image_generator = ImageDataGenerator(rescale=1.0 / 255)
validation_image_generator = ImageDataGenerator(rescale=1.0 / 255)
train_data_gen = train_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=train_dir, shuffle=True, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary')
val_data_gen = validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=validation_dir, shuffle=False, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary')
model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(2, activation='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
EPOCHS = 100
history = model.fit_generator(train_data_gen, steps_per_epoch=int(np.ceil(total_train / float(BATCH_SIZE))), epochs=EPOCHS, validation_data=val_data_gen, validation_steps=int(np.ceil(total_val / float(BATCH_SIZE)))) | code |
18129647/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
BATCH_SIZE = 100
IMG_SHAPE = 150
train_image_generator = ImageDataGenerator(rescale=1.0 / 255)
validation_image_generator = ImageDataGenerator(rescale=1.0 / 255)
val_data_gen = validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=validation_dir, shuffle=False, target_size=(IMG_SHAPE, IMG_SHAPE), class_mode='binary') | code |
18129647/cell_14 | [
"text_plain_output_1.png"
] | import logging
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True) | code |
18129647/cell_22 | [
"text_plain_output_1.png"
] | import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))
num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))
total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val
print('total training cat images:', num_cats_tr)
print('total training dog images:', num_dogs_tr)
print('total validation cat images:', num_cats_val)
print('total validation dog images:', num_dogs_val)
print('--')
print('Total training images:', total_train)
print('Total validation images:', total_val) | code |
18129647/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
fig, axes = plt.subplots(1, 5, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
plt.tight_layout()
plt.show()
plotImages(sample_training_images[:5]) | code |
2000944/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readlines():
x = line.split()
if x[0] == 'atom':
pos_data.append([np.array(x[1:4], dtype=np.float), x[4]])
elif x[0] == 'lattice_vector':
lat_data.append(np.array(x[1:4], dtype=np.float))
A = np.transpose(lat_data)
B = np.linalg.inv(A)
R = pos_data[0][0]
return np.matmul(B, R)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
for c in train.columns:
if c.find('angle') != -1:
print(c)
train[c] = np.radians(train[c])
test[c] = np.radians(test[c])
traindata = np.zeros((train.shape[0], 3))
for i, idx in enumerate(train.id.values):
fn = '../input/train/{}/geometry.xyz'.format(idx)
data = get_xyz_data(fn)
traindata[i, :] = data
testdata = np.zeros((test.shape[0], 3))
for i, idx in enumerate(test.id.values):
fn = '../input/test/{}/geometry.xyz'.format(idx)
data = get_xyz_data(fn)
testdata[i, :] = data
train['a0'] = 0
train['a1'] = 0
train['a2'] = 0
train[['a0', 'a1', 'a2']] = traindata
test['a0'] = 0
test['a1'] = 0
test['a2'] = 0
test[['a0', 'a1', 'a2']] = testdata
train.number_of_total_atoms = np.log(train.number_of_total_atoms)
test.number_of_total_atoms = np.log(test.number_of_total_atoms)
alldata = pd.concat([train, test])
alldata = pd.concat([alldata.drop(['spacegroup'], axis=1), pd.get_dummies(alldata['spacegroup'], prefix='SG')], axis=1)
train = alldata[:train.shape[0]].copy()
test = alldata[train.shape[0]:].copy()
target_fe = np.log1p(train.formation_energy_ev_natom)
target_be = np.log1p(train.bandgap_energy_ev)
del train['formation_energy_ev_natom'], train['bandgap_energy_ev'], train['id'], test['id'] | code |
105190994/cell_42 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:]
df[df.A < 0]
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
iris.groupby('Species').apply(np.mean)
iris = pd.read_csv(CSV_PATH)
iris.to_csv('iris.csv', index=False)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df
missing = df.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing_df = pd.DataFrame({'col': missing.index, 'num_missing': missing.values})
plt.figure(figsize=(14, 7))
plt.title('Missing Map')
f = sns.barplot(y='col', x='num_missing', data=missing_df)
f.figure | code |
105190994/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df[0:3] | code |
105190994/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2 | code |
105190994/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:] | code |
105190994/cell_30 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
iris.groupby('Species').apply(np.mean) | code |
105190994/cell_33 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
iris.groupby('Species').apply(np.mean)
iris = pd.read_csv(CSV_PATH)
iris.to_csv('iris.csv', index=False)
iris.to_numpy() | code |
105190994/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:]
df[df.A < 0]
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
iris.groupby('Species').apply(np.mean)
iris = pd.read_csv(CSV_PATH)
iris.to_csv('iris.csv', index=False)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df
missing = df.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing_df = pd.DataFrame({'col': missing.index, 'num_missing': missing.values})
plt.figure(figsize=(14,7))
plt.title('Missing Map')
f = sns.barplot(y='col', x='num_missing', data=missing_df)
f.figure
corr = df.drop(['Id'], axis=1).corr()
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(11, 11))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
f = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5})
f.figure | code |
105190994/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
iris.info() | code |
105190994/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
df5.fillna(0) | code |
105190994/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:]
df[df.A < 0]
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
iris.groupby('Species').apply(np.mean)
iris = pd.read_csv(CSV_PATH)
iris.to_csv('iris.csv', index=False)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df | code |
105190994/cell_41 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:]
df[df.A < 0]
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5
iris.groupby('Species').apply(np.mean)
iris = pd.read_csv(CSV_PATH)
iris.to_csv('iris.csv', index=False)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df
missing = df.isnull().sum()
missing = missing[missing > 0]
missing.sort_values(inplace=True)
missing_df = pd.DataFrame({'col': missing.index, 'num_missing': missing.values})
sns.barplot(x='col', y='num_missing', data=missing_df) | code |
105190994/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
iris.describe() | code |
105190994/cell_18 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.describe() | code |
105190994/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4
df5 = pd.concat(df_list, axis=1)
df5 | code |
105190994/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
pp.ProfileReport(iris) | code |
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