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stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
17132381/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs)
idx = 1
im, cl = learn.data.dl(DatasetType.Valid).dataset[idx]
cl = int(cl)
im.show(title=f'pred. class: {interp.pred_class[idx]}, actual class: {learn.data.classes[cl]}') | code |
17132381/cell_46 | [
"text_plain_output_1.png"
] | grad = hook_g.stored[0][0].cpu()
grad.shape
grad_chan = grad.mean(1).mean(1)
grad_chan.shape | code |
17132381/cell_24 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
learn.recorder.plot_losses()
learn.recorder.plot_metrics() | code |
17132381/cell_22 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.recorder.plot_losses()
learn.recorder.plot_metrics() | code |
17132381/cell_10 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
df.head(10) | code |
17132381/cell_27 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs)
interp.plot_confusion_matrix(figsize=(12, 12), dpi=60) | code |
17132381/cell_37 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs)
idx = 1
im, cl = learn.data.dl(DatasetType.Valid).dataset[idx]
cl = int(cl)
m = learn.model.eval()
len(m) | code |
17132381/cell_36 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs)
idx = 1
im, cl = learn.data.dl(DatasetType.Valid).dataset[idx]
cl = int(cl)
m = learn.model.eval()
type(m) | code |
2021876/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum() | code |
2021876/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
print('Number rows and columns:', train.shape)
print('Number rows and columns:', test.shape) | code |
2021876/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
fig ,ax = plt.subplots(2,2,figsize=(14,8))
ax1,ax2,ax3,ax4 = ax.flatten()
sns.countplot(df['X0'],palette='rainbow',ax=ax1)
sns.countplot(df['X1'],palette='summer',ax=ax2)
sns.countplot(df['X2'],palette='rainbow',ax=ax3)
sns.countplot(df['X3'],palette='magma',ax=ax4)
fig, ax = plt.subplots(2, 2, figsize=(14, 8))
ax1, ax2, ax3, ax4 = ax.flatten()
sns.countplot(df['X4'], palette='magma', ax=ax1)
sns.countplot(df['X5'], palette='rainbow', ax=ax2)
sns.countplot(df['X6'], palette='summer', ax=ax3)
sns.countplot(df['X8'], palette='magma', ax=ax4) | code |
2021876/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
def OHE(df, columns):
c2, c3 = ([], {})
for c in columns:
c2.append(c)
c3[c] = 'ohe_' + c
df1 = pd.get_dummies(df, prefix=c3, columns=c2, drop_first=True)
return df1
col_ohe = ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']
df1 = OHE(df, col_ohe)
pca = PCA(n_components=None, random_state=seed)
pca.fit(df1.drop(['y', 'ID'], axis=1)) | code |
2021876/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T | code |
2021876/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
plt.figure(figsize=(16, 10))
sns.heatmap(cor, cmap='viridis') | code |
2021876/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
df[other_col].nunique() | code |
2021876/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col | code |
2021876/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
def OHE(df, columns):
c2, c3 = ([], {})
for c in columns:
c2.append(c)
c3[c] = 'ohe_' + c
df1 = pd.get_dummies(df, prefix=c3, columns=c2, drop_first=True)
return df1
col_ohe = ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']
df1 = OHE(df, col_ohe)
df1.head() | code |
2021876/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
plt.figure(figsize=(12, 6))
sns.distplot(train['y'], bins=120)
plt.xlabel('y') | code |
2021876/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col) | code |
2021876/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
fig ,ax = plt.subplots(2,2,figsize=(14,8))
ax1,ax2,ax3,ax4 = ax.flatten()
sns.countplot(df['X0'],palette='rainbow',ax=ax1)
sns.countplot(df['X1'],palette='summer',ax=ax2)
sns.countplot(df['X2'],palette='rainbow',ax=ax3)
sns.countplot(df['X3'],palette='magma',ax=ax4)
fig,ax = plt.subplots(2,2,figsize=(14,8))
ax1,ax2,ax3,ax4 = ax.flatten()
sns.countplot(df['X4'],palette='magma',ax=ax1)
sns.countplot(df['X5'],palette='rainbow',ax=ax2)
sns.countplot(df['X6'],palette='summer',ax=ax3)
sns.countplot(df['X8'],palette='magma',ax=ax4)
plt.figure(figsize=(14, 80))
k = df[bin_col].sum().sort_values()
sns.barplot(k, k.index, orient='h', color='b') | code |
2021876/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
test.isnull().sum().sum() | code |
2021876/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
fig, ax = plt.subplots(2, 2, figsize=(14, 8))
ax1, ax2, ax3, ax4 = ax.flatten()
sns.countplot(df['X0'], palette='rainbow', ax=ax1)
sns.countplot(df['X1'], palette='summer', ax=ax2)
sns.countplot(df['X2'], palette='rainbow', ax=ax3)
sns.countplot(df['X3'], palette='magma', ax=ax4) | code |
2021876/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0]
df = pd.concat([train, test], axis=0)
bin_col = [c for c in df.columns if df[c].nunique() == 2]
len(bin_col)
other_col = [c for c in df.columns if c not in bin_col]
other_col
def category_type(df):
col = df.columns
for i in col:
if 2 < df[i].nunique() <= 53:
df[i] = df[i].astype('category')
category_type(df)
def OHE(df, columns):
c2, c3 = ([], {})
for c in columns:
c2.append(c)
c3[c] = 'ohe_' + c
df1 = pd.get_dummies(df, prefix=c3, columns=c2, drop_first=True)
return df1
col_ohe = ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']
df1 = OHE(df, col_ohe) | code |
50213631/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index()
df_grouped = df.groupby('diet')['cook_time'].mean()
df_grouped.reset_index()
dfgrouped1 = df.groupby(['state', 'flavor_profile']).size().reset_index(name='counts')
dfgrouped1
dfgrouped2 = dfgrouped1.groupby(['state'])['counts'].max()
dfgrouped2 | code |
50213631/cell_13 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df['diet'].value_counts().plot(kind='bar') | code |
50213631/cell_4 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist() | code |
50213631/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index()
df_grouped = df.groupby('diet')['cook_time'].mean()
df_grouped.reset_index()
dfgrouped1 = df.groupby(['state', 'flavor_profile']).size().reset_index(name='counts')
dfgrouped1 | code |
50213631/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.head() | code |
50213631/cell_11 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique() | code |
50213631/cell_19 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index()
df_grouped = df.groupby('diet')['cook_time'].mean()
df_grouped.reset_index()
df['flavor_profile'].unique() | code |
50213631/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 |
50213631/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df['flavor_profile'].unique() | code |
50213631/cell_15 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index() | code |
50213631/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.info() | code |
50213631/cell_17 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index()
df_grouped = df.groupby('diet')['cook_time'].mean()
df_grouped.reset_index() | code |
50213631/cell_22 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique()
df_grouped = df.groupby('diet')['prep_time'].mean()
df_grouped.reset_index()
df_grouped = df.groupby('diet')['cook_time'].mean()
df_grouped.reset_index()
dfgrouped1 = df.groupby(['state', 'flavor_profile']).size().reset_index(name='counts')
dfgrouped1
dfgrouped2 = dfgrouped1.groupby(['state'])['counts'].max()
dfgrouped2
dfgrouped1.groupby(['state', 'flavor_profile', 'counts'], as_index=False).max() | code |
50213631/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique() | code |
50213631/cell_12 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].unique()
df = df[df['state'] != '-1']
df['state'].unique() | code |
33104934/cell_13 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import plotly.figure_factory as ff
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine')
Z = linkage(y=Y)
ff.create_dendrogram(Z) | code |
33104934/cell_9 | [
"text_html_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine')
Z = linkage(y=Y)
F = fcluster(Z, t=0.2)
F | code |
33104934/cell_4 | [
"text_plain_output_1.png"
] | import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc | code |
33104934/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets | code |
33104934/cell_11 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine')
Z = linkage(y=Y)
F = fcluster(Z, t=0.2)
F
df_texto = pd.DataFrame({'texto': norm_doc, 'cluster': F}, columns=['texto', 'cluster'])
filtro = df_texto.cluster == 2937
df_texto[filtro] | code |
33104934/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
import scipy.cluster.hierarchy as sch
from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import plotly.figure_factory as ff
from scipy import stats
import io
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33104934/cell_7 | [
"text_html_output_1.png"
] | from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine') | code |
33104934/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc | code |
33104934/cell_10 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine')
Z = linkage(y=Y)
F = fcluster(Z, t=0.2)
F
stats.mode(F) | code |
33104934/cell_12 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normalize_document(doc):
doc = re.sub('[^a-zA-Z\\s]', '', doc)
doc = doc.lower()
doc = doc.strip()
tokens = wpt.tokenize(doc)
filtered_tokens = [token for token in tokens if token not in stop_words]
doc = ' '.join(filtered_tokens)
return doc
normalize_doc = np.vectorize(normalize_document)
norm_doc = normalize_doc(doc)
norm_doc
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
def embed(input):
return model(input)
X = embed(norm_doc)
Y = pdist(X, 'cosine')
Z = linkage(y=Y)
F = fcluster(Z, t=0.2)
F
df_texto = pd.DataFrame({'texto': norm_doc, 'cluster': F}, columns=['texto', 'cluster'])
filtro = df_texto.cluster == 2937
df_texto[filtro]
df_texto = pd.DataFrame({'texto': norm_doc, 'cluster': F}, columns=['texto', 'cluster'])
filtro = df_texto.cluster == 15
df_texto[filtro] | code |
33104934/cell_5 | [
"text_html_output_1.png"
] | import tensorflow_hub as hub
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
print('module %s loaded' % module_url)
def embed(input):
return model(input) | code |
130021822/cell_21 | [
"text_plain_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['HeatingQC'].value_counts() | code |
130021822/cell_13 | [
"text_plain_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['OverallCond'].value_counts() | code |
130021822/cell_9 | [
"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)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
train_df['LotFrontage'].hist() | code |
130021822/cell_25 | [
"image_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(x='SalePrice', y='GarageArea') | code |
130021822/cell_23 | [
"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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
sns.catplot(kind='box', data=train_df, x='CentralAir', y='SalePrice')
plt.show() | code |
130021822/cell_6 | [
"image_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum() | code |
130021822/cell_26 | [
"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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
sns.stripplot(data=train_df, x='PavedDrive', y='SalePrice', jitter=True, dodge=True) | code |
130021822/cell_19 | [
"image_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(y='SalePrice', x='TotalBsmtSF') | code |
130021822/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 |
130021822/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(y='SalePrice', x='MasVnrArea') | code |
130021822/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='OverallCond', data=train_df) | code |
130021822/cell_16 | [
"text_plain_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['ExterCond'].value_counts() | code |
130021822/cell_3 | [
"image_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns | code |
130021822/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='ExterCond', data=train_df) | code |
130021822/cell_31 | [
"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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
features = ['LotArea', 'LotFrontage', 'HouseStyle', 'OverallCond', 'ExterCond', 'TotalBsmtSF', 'CentralAir', 'GarageArea', 'GrLivArea', 'PavedDrive']
X = train_df[features]
sns.heatmap(X.corr()) | code |
130021822/cell_14 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='HouseStyle', data=train_df) | code |
130021822/cell_22 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.violinplot(data=train_df, y='SalePrice', x='HeatingQC') | code |
130021822/cell_10 | [
"text_plain_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['Utilities'].value_counts() | code |
130021822/cell_27 | [
"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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
sns.histplot(data=train_df, x='GrLivArea', y='SalePrice', kde=True) | code |
130021822/cell_12 | [
"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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['HouseStyle'].value_counts() | code |
130021822/cell_5 | [
"image_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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.describe() | code |
105187067/cell_13 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
df_total['country'].value_counts() | code |
105187067/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.head() | code |
105187067/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
test.head() | code |
105187067/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
df_train = one_hot_encoded_data[~one_hot_encoded_data.num_sold.isnull()]
df_test = one_hot_encoded_data[one_hot_encoded_data.num_sold.isnull()].drop(['num_sold'], axis=1)
X_train = df_train.drop('num_sold', axis=1)
y_train = df_train['num_sold']
X_test = df_test
def My_CV(_model):
cv_score = round(cross_val_score(_model, X_train, y_train, cv=5, scoring='r2').mean(), 4)
xgb = XGBRegressor(random_state=2, objective='reg:squarederror')
My_CV(xgb) | code |
105187067/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
df_train = one_hot_encoded_data[~one_hot_encoded_data.num_sold.isnull()]
df_test = one_hot_encoded_data[one_hot_encoded_data.num_sold.isnull()].drop(['num_sold'], axis=1)
X_train = df_train.drop('num_sold', axis=1)
y_train = df_train['num_sold']
X_test = df_test
X_test.shape | code |
105187067/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
train.head() | code |
105187067/cell_2 | [
"text_plain_output_1.png"
] | import warnings
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, PowerTransformer
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, cross_val_predict
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, AdaBoostRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from xgboost import XGBRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import r2_score
from sklearn.compose import TransformedTargetRegressor
import warnings
warnings.filterwarnings('ignore') | code |
105187067/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
df_train = one_hot_encoded_data[~one_hot_encoded_data.num_sold.isnull()]
df_test = one_hot_encoded_data[one_hot_encoded_data.num_sold.isnull()].drop(['num_sold'], axis=1)
X_train = df_train.drop('num_sold', axis=1)
y_train = df_train['num_sold']
X_test = df_test
X_test.head() | code |
105187067/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 |
105187067/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
train.info() | code |
105187067/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
one_hot_encoded_data.head() | code |
105187067/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, AdaBoostRegressor
from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
df_train = one_hot_encoded_data[~one_hot_encoded_data.num_sold.isnull()]
df_test = one_hot_encoded_data[one_hot_encoded_data.num_sold.isnull()].drop(['num_sold'], axis=1)
X_train = df_train.drop('num_sold', axis=1)
y_train = df_train['num_sold']
X_test = df_test
def My_CV(_model):
cv_score = round(cross_val_score(_model, X_train, y_train, cv=5, scoring='r2').mean(), 4)
Hgb = HistGradientBoostingRegressor(random_state=2)
My_CV(Hgb) | code |
105187067/cell_14 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
df_total.info() | code |
105187067/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, AdaBoostRegressor
from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', axis=1, inplace=True)
df_total['date'] = pd.to_datetime(df_total['date'], format='%Y-%m-%d')
df_total['year'] = df_total['date'].dt.year
df_total['month'] = df_total['date'].dt.month
df_total['day'] = df_total['date'].dt.day
df_total['day_of_week'] = df_total['date'].dt.day_of_week
df_total['day_of_year'] = df_total['date'].dt.day_of_year
df_total['is_weekend'] = np.where(df_total['day_of_week'].isin([5, 6]), 1, 0)
df_total.drop('date', axis=1, inplace=True)
one_hot_encoded_data = pd.get_dummies(df_total, columns=['country', 'store', 'product'], drop_first=True)
df_train = one_hot_encoded_data[~one_hot_encoded_data.num_sold.isnull()]
df_test = one_hot_encoded_data[one_hot_encoded_data.num_sold.isnull()].drop(['num_sold'], axis=1)
X_train = df_train.drop('num_sold', axis=1)
y_train = df_train['num_sold']
X_test = df_test
def My_CV(_model):
cv_score = round(cross_val_score(_model, X_train, y_train, cv=5, scoring='r2').mean(), 4)
rf = RandomForestRegressor(random_state=2)
My_CV(rf) | code |
72107191/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os, glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
from statsmodels.distributions.empirical_distribution import ECDF
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
print('Setup Complete') | code |
72107191/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
districts_info.shape
districts_info.columns | code |
72107191/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
engagement_data.shape
engagement_data.columns
print(f'Number of rows: {engagement_data.shape[0]}; Number of columns: {engagement_data.shape[1]}; No of missing values: {sum(engagement_data.isna().sum())}') | code |
72107191/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
districts_info.shape
districts_info.columns
districts_info.isnull().sum()
districts_info.duplicated().any() | code |
72107191/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
engagement_data.shape
engagement_data.columns | code |
72107191/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.shape
product_info.columns
product_info.isnull().sum()
product_info.info() | code |
72107191/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
districts_info.shape
districts_info.columns
districts_info.isnull().sum()
districts_info.info() | code |
72107191/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
districts_info.shape
districts_info.columns
print(f'Number of rows: {districts_info.shape[0]}; Number of columns: {districts_info.shape[1]}; No of missing values: {sum(districts_info.isna().sum())}') | code |
72107191/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.shape
product_info.columns
product_info.isnull().sum()
product_info.duplicated().any()
product_info = product_info.rename(columns={'Provider/Company Name': 'provider'})
product_info = product_info.rename(columns={'Primary Essential Function': 'essential function'})
plt.figure(figsize=(8, 6))
sns.countplot(x='essential function', order=product_info['essential function'].value_counts().index[:3], data=product_info, color='blue')
plt.title("product's essential function") | code |
72107191/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.shape
product_info.columns
product_info.isnull().sum() | code |
72107191/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
engagement_data.head()
engagement_data.tail() | code |
72107191/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
districts_info.shape
districts_info.columns
districts_info.isnull().sum() | code |
72107191/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.head()
product_info.tail() | code |
72107191/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.shape
product_info.columns | code |
72107191/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
engagement_data.shape
engagement_data.columns
engagement_data.isnull().sum()
engagement_data.duplicated().any() | code |
72107191/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = district_id
all_files.append(df)
engagement_data = pd.concat(all_files)
engagement_data = engagement_data.reset_index(drop=True)
product_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_info = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
product_info.shape
product_info.columns
print(f'Number of rows: {product_info.shape[0]}; Number of columns: {product_info.shape[1]}; No of missing values: {sum(product_info.isna().sum())}') | code |
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