path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2019512/cell_5 | [
"image_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
def plot_dist(name, frame, color='green'):
name0 = '{}_'.format(name)
ticklabel_op = True
if name0 not in frame.columns:
name0 = name
ticklabel_op = False
data_count = len(frame[name0].unique())
if data_count > 3:
sns.dist... | code |
50212972/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
data = pd.read_csv('../input/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count()
plt.figure(figsize=(10, 5))
plt.title('Survived vs. Sex')
sns.coun... | code |
50212972/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/titanic/train.csv')
data.head() | code |
50212972/cell_20 | [
"text_plain_output_2.png",
"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/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count()
hm_data = data[['Survived', 'Pclass']]
hm_data = hm_data.groupby([... | code |
50212972/cell_19 | [
"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/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count()
pd.crosstab([data.Sex, data.Survived], data.Pclass, margins=True).style.background_gradient(cmap='winter') | code |
50212972/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50212972/cell_8 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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/titanic/train.csv')
data.isnull().sum()
plt.figure(figsize=(10, 5))
sns.countplot('Survived', data=data) | code |
50212972/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/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count()
hm_data = data[['Survived', 'Pclass']]
hm_data = hm_data.groupby([... | code |
50212972/cell_17 | [
"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
data = pd.read_csv('../input/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count()
hm_data = data[['Survived', 'Pclass']]
hm_data = hm_data.groupby([... | code |
50212972/cell_12 | [
"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/titanic/train.csv')
data.isnull().sum()
data.groupby(['Sex', 'Survived'])['Survived'].count() | code |
50212972/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data.isnull().sum() | code |
129010000/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/co... | code |
129010000/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/content/drive/MyDrive/HMD_project/new/embedding_train_img_norm.npy')
txt_input = np.load('/content/drive/MyDrive/HMD_project/new/lsa_bow_train_text.npy')... | code |
129010000/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
img_label = np.load('/content/drive/MyDrive/HMD_project/new/embedding_train_img_norm.npy')
txt_input = np.load('/content/drive/MyDrive/HMD_project/new/lsa_bow_train_text.npy')
img = img_label[:, 0:-1]
label = img_label[:, -1]
txt = txt_input
img_txt = np.concatenate((img, txt), axis=1)
x = img_txt
y... | code |
129010000/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
img_label = np.load('/content/drive/MyDrive/HMD_project/new/embedding_train_img_norm.npy')
txt_input = np.load('/content/drive/MyDrive/HMD_project/new/lsa_bow_train_text.npy')
img = img_label[:, 0:-1]
label = img_label[:, -1]
txt = txt_input
img_txt = np.concatenate((img, txt), axis=1)
x = img_txt
y... | code |
129010000/cell_2 | [
"text_plain_output_1.png"
] | from google.colab import drive
from google.colab import drive
drive.mount('/content/drive') | code |
129010000/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/co... | code |
129010000/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/co... | code |
129010000/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/co... | code |
129010000/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/content/drive/MyDrive/HMD_project/new/embedding_train_i... | code |
129010000/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
import numpy as np
img_label = np.load('/co... | code |
73061941/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import numpy as np
import numpy as np # linear algebra
import os
import os
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import os
class EncoderBlock(nn.Module):
def __init__(self, in_c... | code |
73061941/cell_12 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
class EncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, dropout=0, pool=True) -> None:
super(EncoderBlo... | code |
72112151/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
72112151/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
72112151/cell_11 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
model = XGBRegressor(n_estimators=1000, learning_rate=0.01, n_jobs=4)
model.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False) | code |
72112151/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if... | code |
72112151/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor(n_estimators=1000, learning_rate=0.01, n_jobs=4)
model.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
preds_valid = model.predict(X_valid)
print(mean_squared_error(y_vali... | code |
72112151/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.info() | code |
88086160/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('./train.csv')
train.index = train['image'].copy()
train['image'] = train['image'].str[:-3] + 'bmp'
train['bbox'] = pd.read_csv('../input/happywhale-boundingbox-yolov5/train.csv', index_col='image')['bbox']
train['bbox'] = train['bbox'].fillna('[]').map(eval)
train | code |
88086160/cell_7 | [
"text_plain_output_1.png"
] | import cv2
import os
import pandas as pd
IMAGE_SIZE = 224
train = pd.read_csv('./train.csv')
train.index = train['image'].copy()
train['image'] = train['image'].str[:-3] + 'bmp'
train['bbox'] = pd.read_csv('../input/happywhale-boundingbox-yolov5/train.csv', index_col='image')['bbox']
train['bbox'] = train['bbox'].f... | code |
88086160/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('./train.csv')
train.index = train['image'].copy()
train['image'] = train['image'].str[:-3] + 'bmp'
train['bbox'] = pd.read_csv('../input/happywhale-boundingbox-yolov5/train.csv', index_col='image')['bbox']
train['bbox'] = train['bbox'].fillna('[]').map(eval)
train
sample_submi... | code |
17116992/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import IsolationForest
from sklearn.metrics import classification_report,accuracy_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
fraud_ratio = float(data['Class'][data['Class'] == 1].shape[0]) / data.shape[0]
X = data... | code |
17116992/cell_4 | [
"text_plain_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/creditcard.csv')
sns.countplot(data['Class']) | code |
17116992/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/creditcard.csv')
fraud_ratio = float(data['Class'][data['Class'] == 1].shape[0]) / data.shape[0]
data.head() | code |
17116992/cell_11 | [
"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/creditcard.csv')
fraud_ratio = float(data['Class'][data['Class'] == 1].shape[0]) / data.shape[0]
X = data.iloc[:, :-1]
Y = data['Class']
(Y.shape, X.shape) | code |
17116992/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17116992/cell_7 | [
"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)
data = pd.read_csv('../input/creditcard.csv')
fraud_ratio = float(data['Class'][data['Class'] == 1].shape[0]) / data.shape[0]
data['Class'].value_counts() | code |
17116992/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data.info() | code |
1005390/cell_6 | [
"text_plain_output_1.png"
] | from sklearn import svm
clf = svm.SVC(kernel='rbf', gamma=0.01, C=10)
clf.fit(train_images, train_labels.values.ravel())
clf.score(test_images, test_labels) | code |
1005390/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt, matplotlib.image as mpimg
from sklearn.cross_validation import train_test_split
from sklearn import svm
import numpy as np | code |
106205797/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
print(c.dty... | code |
106205797/cell_4 | [
"text_plain_output_1.png"
] | pip install numpy | code |
106205797/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print('rank: ', b.ndim) | code |
106205797/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(b.shape) | code |
106205797/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(b) | code |
106205797/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print('rank: ', a.ndim) | code |
106205797/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a.shape) | code |
106205797/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a.dtype) | code |
106205797/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
print(a)
print(type(a)) | code |
106205797/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
start = time.time()
np.mean(x)
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
c = np.array(['Hello', 'World'])
d = np.arr... | code |
106205797/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import time
import time
import numpy as np
x = np.random.random(100000000)
start = time.time()
sum(x) / len(x)
print('using built-in python function: ', time.time() - start)
start = time.time()
np.mean(x)
print('using NumPy: ', time.time() - start) | code |
32071774/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
train_df.info() | code |
32071774/cell_2 | [
"text_plain_output_1.png"
] | pip install pycountry_convert | code |
32071774/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 |
32071774/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
test_df.info() | code |
32071774/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
print('Min train date: ', train_df['Date'].min())
print('Max train dat... | code |
32071774/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
pop_info = pd.read_csv('../input/population-by-country-2020/population... | code |
32071774/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
train_df.head() | code |
49125428/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.column... | code |
49125428/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
question = data.iloc[0, :]
data = data.iloc[1:, :]
fig,ax=plt.subplots(1,1,figsize=(8,5),dpi=200)
dataQ1=data['Q1'].value_counts().sort_index()
ax.bar(dataQ1.index,dat... | code |
49125428/cell_4 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape | code |
49125428/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.column... | code |
49125428/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.columns], columns=['name', 'n... | code |
49125428/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.columns], columns=['name', 'nunique', 'missing', 'dtype', 'val... | code |
49125428/cell_2 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv') | code |
49125428/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
question = data.iloc[0, :]
data = data.iloc[1:, :]
fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=200)
dataQ1 = data['Q1'].value_counts().sort_index()
ax.bar(dataQ1.in... | code |
49125428/cell_19 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.columns], columns=['name', 'nunique', 'missing', 'dtype', 'val... | code |
49125428/cell_7 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
data.head() | code |
49125428/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
question = data.iloc[0, :]
data = data.iloc[1:, :]
fig,ax=plt.subplots(1,1,figsize=(8,5),dpi=200)
dataQ1=data['Q1'].value_counts().sort_index()
ax.bar(dataQ1.index,dat... | code |
49125428/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
question = data.iloc[0, :]
data = data.iloc[1:, :]
fig,ax=plt.subplots(1,1,figsize=(8,5),dpi=200)
dataQ1=data['Q1'].value_counts().sort_index()
ax.bar(dataQ1.index,dat... | code |
49125428/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.head() | code |
49125428/cell_24 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.columns], columns=['name', 'nunique', 'missing', 'dtype', 'val... | code |
49125428/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
question = data.iloc[0, :]
data = data.iloc[1:, :]
fig,ax=plt.subplots(1,1,figsize=(8,5),dpi=200)
dataQ1=data['Q1'].value_counts().sort_index()
ax.bar(dataQ1.index,dat... | code |
49125428/cell_22 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
data.shape
def datainfo(df):
return pd.DataFrame([(col, df[col].nunique(), df[col].isna().sum(), df[col].dtype, df[col].unique()[:5]) for col in df.columns], columns=['name', 'nunique', 'missing', 'dtype', 'val... | code |
50234958/cell_4 | [
"text_html_output_1.png"
] | from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
bert_version = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(bert_version) | code |
50234958/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
submission = pd.read_csv('/kaggle/input/quora-question-pairs/sample_submission.csv.zip')
submission.head() | code |
50234958/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 |
50234958/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
train_df.head() | code |
2016683/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
INPUT_PATH = '../input/'
childPref = pd.read_csv(INPUT_PATH + 'child_wishlist.csv', header=None).as_matrix()[:, 1:]
santaPref = pd.read_csv(INPUT_PATH + 'gift_goodkids.csv', header=None).as_matrix()[:, 1:]
numChildren = childPref.shape[0]
numGifts = santaPref.shape[0]
numGiftsP... | code |
2016683/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
print('loading data...')
INPUT_PATH = '../input/'
childPref = pd.read_csv(INPUT_PATH + 'child_wishlist.csv', header=None).as_matrix()[:, 1:]
santaPref = pd.read_csv(INPUT_PATH + 'gift_goodkids.csv', header=None).as_matrix()[:, 1:] | code |
2016683/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
INPUT_PATH = '../input/'
childPref = pd.read_csv(INPUT_PATH + 'child_wishlist.csv', header=None).as_matrix()[:, 1:]
santaPref = pd.read_csv(INPUT_PATH + 'gift_goodkids.csv', header=None).as_matrix()[:, 1:]
numChildren = childPref.shape[0]
numGifts = santaPref.shape[0]
numGiftsP... | code |
2016683/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | out = open('submission_heuristic.csv', 'w')
out.write('ChildId,GiftId\n')
for i in range(len(pred)):
out.write(str(i) + ',' + str(pred[i]) + '\n')
out.close() | code |
90122454/cell_21 | [
"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_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Name.describe() | code |
90122454/cell_25 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train['Sex'].value_counts() | code |
90122454/cell_30 | [
"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
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
sns.displot(data=data_train.Age, kde... | code |
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