path
stringlengths 13
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sequencelengths 1
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|---|---|---|---|
90127845/cell_8 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.join(root, 'test.csv'))
test_df['datetime'] = pd.to_datetime(test_df.time)
test_df['date'] = test_df.datetime.dt.date
test_df['time'] = test_df.datetime.dt.time
sep_30 = datetime.date(1991, 9, 30)
mondays = train_df[train_df.datetime.dt.dayofweek == 0]
mondays['is_morning'] = mondays.datetime.dt.hour < 12
labor_day = datetime.date(1991, 9, 2)
memorial_day = datetime.date(1991, 5, 27)
mondays = mondays[(mondays.date != labor_day) & (mondays.date != memorial_day)]
mondays[mondays.datetime.dt.date < sep_30].groupby('date').congestion.mean().plot()
plt.title('Congestion by date')
plt.ylabel('avg daily congestion')
plt.tight_layout()
plt.show() | code |
90127845/cell_17 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.join(root, 'test.csv'))
test_df['datetime'] = pd.to_datetime(test_df.time)
test_df['date'] = test_df.datetime.dt.date
test_df['time'] = test_df.datetime.dt.time
sep_30 = datetime.date(1991, 9, 30)
mondays = train_df[train_df.datetime.dt.dayofweek == 0]
mondays['is_morning'] = mondays.datetime.dt.hour < 12
labor_day = datetime.date(1991, 9, 2)
memorial_day = datetime.date(1991, 5, 27)
mondays = mondays[(mondays.date != labor_day) & (mondays.date != memorial_day)]
plt.tight_layout()
plt.tight_layout()
for (x, y), G in mondays.groupby(['x', 'y']):
plt.tight_layout()
for direction, G in mondays.groupby('direction'):
plt.tight_layout()
train = mondays[mondays.datetime.dt.date < sep_30]
models = {}
for (x, y, direction), G in train.groupby(['x', 'y', 'direction']):
morning_data = G[G.is_morning]
afternoon_data = G[~G.is_morning]
X = morning_data.pivot(index='date', columns='time', values='congestion').reset_index().drop(columns=['date'])
Y = afternoon_data.pivot(index='date', columns='time', values='congestion').reset_index().drop(columns=['date'])
model = KNeighborsRegressor()
models[x, y, direction] = model.fit(X, Y) | code |
90127845/cell_14 | [
"image_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.join(root, 'test.csv'))
test_df['datetime'] = pd.to_datetime(test_df.time)
test_df['date'] = test_df.datetime.dt.date
test_df['time'] = test_df.datetime.dt.time
sep_30 = datetime.date(1991, 9, 30)
mondays = train_df[train_df.datetime.dt.dayofweek == 0]
mondays['is_morning'] = mondays.datetime.dt.hour < 12
labor_day = datetime.date(1991, 9, 2)
memorial_day = datetime.date(1991, 5, 27)
mondays = mondays[(mondays.date != labor_day) & (mondays.date != memorial_day)]
plt.tight_layout()
plt.tight_layout()
for (x, y), G in mondays.groupby(['x', 'y']):
plt.tight_layout()
for direction, G in mondays.groupby('direction'):
G.boxplot(by='time', column='congestion', rot=90, figsize=(12, 5))
plt.title(direction)
plt.tight_layout()
plt.plot() | code |
90127845/cell_10 | [
"text_html_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.join(root, 'test.csv'))
test_df['datetime'] = pd.to_datetime(test_df.time)
test_df['date'] = test_df.datetime.dt.date
test_df['time'] = test_df.datetime.dt.time
sep_30 = datetime.date(1991, 9, 30)
mondays = train_df[train_df.datetime.dt.dayofweek == 0]
mondays['is_morning'] = mondays.datetime.dt.hour < 12
labor_day = datetime.date(1991, 9, 2)
memorial_day = datetime.date(1991, 5, 27)
mondays = mondays[(mondays.date != labor_day) & (mondays.date != memorial_day)]
plt.tight_layout()
mondays[mondays.is_morning].groupby('date').congestion.mean().plot(label='Morning')
mondays[~mondays.is_morning].groupby('date').congestion.mean().plot(label='Afternoon')
plt.title('Congestion by date')
plt.ylabel('avg daily congestion')
plt.legend()
plt.tight_layout()
plt.show() | code |
90127845/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.read_csv(os.path.join(root, 'test.csv'))
test_df['datetime'] = pd.to_datetime(test_df.time)
test_df['date'] = test_df.datetime.dt.date
test_df['time'] = test_df.datetime.dt.time
sep_30 = datetime.date(1991, 9, 30)
mondays = train_df[train_df.datetime.dt.dayofweek == 0]
mondays['is_morning'] = mondays.datetime.dt.hour < 12
labor_day = datetime.date(1991, 9, 2)
memorial_day = datetime.date(1991, 5, 27)
mondays = mondays[(mondays.date != labor_day) & (mondays.date != memorial_day)]
plt.tight_layout()
plt.tight_layout()
for (x, y), G in mondays.groupby(['x', 'y']):
G.boxplot(by='time', column='congestion', rot=90, figsize=(12, 5))
plt.title('{}, {}'.format(x, y))
plt.tight_layout()
plt.plot() | code |
16154359/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.mixture import GaussianMixture
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols = [c for c in train.columns if c not in ['id', 'target', 'target_pred', 'wheezy-copper-turtle-magic']]
cols = [c for c in default_cols]
sub = pd.read_csv('../input/sample_submission.csv')
sub.to_csv('submission.csv', index=False)
(train.shape, test.shape)
if sub.shape[0] == magicNum:
[].shape
preds = np.zeros(len(test))
train_err = np.zeros(512)
test_err = np.zeros(512)
for i in range(512):
X = train[train['wheezy-copper-turtle-magic'] == i].copy()
Y = X.pop('target').values
X_test = test[test['wheezy-copper-turtle-magic'] == i].copy()
idx_train = X.index
idx_test = X_test.index
X.reset_index(drop=True, inplace=True)
X = X[cols].values
X_test = X_test[cols].values
vt = VarianceThreshold(threshold=2).fit(X)
X = vt.transform(X)
X_test = vt.transform(X_test)
X_all = np.concatenate([X, X_test])
train_size = len(X)
test1_size = test[:131073][test[:131073]['wheezy-copper-turtle-magic'] == i].shape[0]
compo_cnt = 6
for ii in range(30):
gmm = GaussianMixture(n_components=compo_cnt, init_params='random', covariance_type='full', max_iter=100, tol=1e-10, reg_covar=0.0001).fit(X_all)
labels = gmm.predict(X_all)
cntStd = np.std([len(labels[labels == j]) for j in range(compo_cnt)])
if round(cntStd, 4) == 0.4714:
check_labels = labels[:train_size]
cvt_labels = np.zeros(len(labels))
for iii in range(compo_cnt):
mean_val = Y[check_labels == iii].mean()
mean_val = 1 if mean_val > 0.5 else 0
cvt_labels[labels == iii] = mean_val
train_err[i] = len(Y[Y != cvt_labels[:train_size]])
if train_err[i] >= 10 and train_err[i] <= 15:
train_err[i] = 12.5
exp_err = max(0, (25 - train_err[i]) / (train_size + test1_size))
for iii in range(compo_cnt):
mean_val = Y[check_labels == iii].mean()
mean_val = 1 - exp_err if mean_val > 0.5 else exp_err
cvt_labels[labels == iii] = mean_val
preds[idx_test] = cvt_labels[train_size:]
break
sub['target'] = preds
sub.to_csv('submission.csv', index=False) | code |
16154359/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import roc_auc_score
y_perfect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_flliped = [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]
roc_auc_score(y_perfect, y_flliped)
y_preds = [0.33, 0.33, 0.33, 0.5, 0.5, 0, 0, 0, 0, 0, 1, 1, 0.5, 0.5, 1, 1, 1, 0.66, 0.66, 0.66]
roc_auc_score(y_flliped, y_preds) | code |
16154359/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols = [c for c in train.columns if c not in ['id', 'target', 'target_pred', 'wheezy-copper-turtle-magic']]
cols = [c for c in default_cols]
sub = pd.read_csv('../input/sample_submission.csv')
sub.to_csv('submission.csv', index=False)
(train.shape, test.shape) | code |
16154359/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.metrics import roc_auc_score
y_perfect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_flliped = [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]
roc_auc_score(y_perfect, y_flliped) | code |
106204398/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='n_cores', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106204398/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.describe() | code |
106204398/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test)
confusion_matrix(y_test, clf.predict(X_test))
kf = KFold(n_splits=5)
kf.get_n_splits(X)
training_scores = []
testing_scores = []
for fold, (train_index, test_index) in enumerate(kf.split(X)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
print(f'Fold {fold + 1} -> The score of the training data set is: ', clf.score(X_train, y_train))
print(f'Fold {fold + 1} -> The score of the testing (out of fold) data set is: ', clf.score(X_test, y_test))
training_scores.append(clf.score(X_train, y_train))
testing_scores.append(clf.score(X_test, y_test))
print('\n')
print(f'The average training set accuracy is: {sum(training_scores) / len(training_scores)}')
print(f'The average testing set accuracy is: {sum(testing_scores) / len(testing_scores)}') | code |
106204398/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any() | code |
106204398/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test)
confusion_matrix(y_test, clf.predict(X_test)) | code |
106204398/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LogisticRegression
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106204398/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x='blue', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106204398/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
106204398/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='wifi', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106204398/cell_15 | [
"text_plain_output_1.png"
] | X_test | code |
106204398/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.head() | code |
106204398/cell_17 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train) | code |
106204398/cell_14 | [
"text_plain_output_1.png"
] | X_train | code |
106204398/cell_22 | [
"image_output_1.png"
] | from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
kf = KFold(n_splits=5)
kf.get_n_splits(X) | code |
106204398/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(), cmap='BrBG', annot=True, linewidths=2.0) | code |
106204398/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
print(X.shape)
print(y.shape) | code |
106204398/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.info() | code |
106211686/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum() | code |
106211686/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False) | code |
106211686/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.head() | code |
106211686/cell_56 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn.metrics import explained_variance_score, r2_score
explained_variance_score(reg_pred, y_test) | code |
106211686/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train['country'].value_counts() | code |
106211686/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['store'].unique() | code |
106211686/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train['Covid'].value_counts() | code |
106211686/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train[60000:60005] | code |
106211686/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train.groupby(train['Covid'])['num_sold'].mean()
train.drop('date', axis=1, inplace=True)
train.drop('country', axis=1, inplace=True)
train.drop('store', axis=1, inplace=True)
train.drop('product', axis=1, inplace=True)
train | code |
106211686/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train.groupby(train['Covid'])['num_sold'].mean() | code |
106211686/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.metrics import explained_variance_score, r2_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
tree = DecisionTreeRegressor(splitter='random', max_depth=20, max_features='sqrt')
tree.fit(X_train, y_train)
tree_pred = tree.predict(X_test)
print(explained_variance_score(tree_pred, y_test), r2_score(y_test, tree_pred)) | code |
106211686/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train.groupby(train['Covid'])['num_sold'].mean()
train.drop('date', axis=1, inplace=True)
train.drop('country', axis=1, inplace=True)
train.drop('store', axis=1, inplace=True)
train.drop('product', axis=1, inplace=True)
train.drop('date_time', axis=1, inplace=True)
train.drop('row_id', axis=1, inplace=True)
train.columns | code |
106211686/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 |
106211686/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['country'].unique() | code |
106211686/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train.groupby(train['Covid'])['num_sold'].mean()
train.drop('date', axis=1, inplace=True)
train.drop('country', axis=1, inplace=True)
train.drop('store', axis=1, inplace=True)
train.drop('product', axis=1, inplace=True)
train.drop('date_time', axis=1, inplace=True)
train.drop('row_id', axis=1, inplace=True)
train | code |
106211686/cell_59 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn import linear_model
ridge = linear_model.Ridge(alpha=0.5)
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)
explained_variance_score(ridge_pred, y_test)
from sklearn import linear_model
lasso = linear_model.Lasso(alpha=0.35)
lasso.fit(X_train, y_train)
lasso_pred = lasso.predict(X_test)
explained_variance_score(lasso_pred, y_test) | code |
106211686/cell_58 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn import linear_model
ridge = linear_model.Ridge(alpha=0.5)
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)
explained_variance_score(ridge_pred, y_test) | code |
106211686/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['product'].unique() | code |
106211686/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False) | code |
106211686/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
for x in train[train['year'] == 2020]['month'].index:
if train['month'].loc[x] == 1:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 6:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 7:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 8:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 9:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 10:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 11:
train['Covid'].loc[x] = 0
elif train['month'].loc[x] == 12:
train['Covid'].loc[x] = 0
else:
train['Covid'].loc[x] = 1 | code |
106211686/cell_3 | [
"text_plain_output_1.png"
] | import random
import random
random.seed(10)
print(random.random()) | code |
106211686/cell_46 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['dayofweek'])['num_sold'].mean().sort_values(ascending=False)
train.isnull().sum()
train.groupby(train['Covid'])['num_sold'].mean()
train.head() | code |
106211686/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['month'])['num_sold'].mean().sort_values(ascending=False)
train.groupby(train['year'])['num_sold'].mean().sort_values(ascending=False) | code |
106211686/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.groupby(train['day'])['num_sold'].mean().sort_values(ascending=False) | code |
106211686/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train.info() | code |
33106742/cell_25 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
plt.plot(price_series)
plt.show() | code |
33106742/cell_23 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
plt.plot(price_series)
plt.show() | code |
33106742/cell_30 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points = []
larger_number_of_trials = 9001
for i in range(larger_number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points.append(price_series[-1])
expected_ending_price_point = round(np.mean(ending_price_points), 2)
top_ten = np.percentile(ending_price_points, 100 - 10)
bottom_ten = np.percentile(ending_price_points, 10)
print('Top 10% : ', str(round(top_ten, 2)))
print('Bottom 10% : ', str(round(bottom_ten, 2))) | code |
33106742/cell_29 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points = []
larger_number_of_trials = 9001
for i in range(larger_number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points.append(price_series[-1])
expected_ending_price_point = round(np.mean(ending_price_points), 2)
population_mean = (cagr + 1) * ibm['Adj Close'][-1]
print('Sample Mean : ', str(expected_ending_price_point))
print('Population Mean: ', str(round(population_mean, 2)))
print('Percent Difference : ', str(round((population_mean - expected_ending_price_point) / population_mean * 100, 2)), '%') | code |
33106742/cell_26 | [
"image_output_2.png",
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points = []
larger_number_of_trials = 9001
for i in range(larger_number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
plt.plot(price_series)
ending_price_points.append(price_series[-1])
plt.show()
plt.hist(ending_price_points, bins=50)
plt.show() | code |
33106742/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import math
import matplotlib.pyplot as plt
import numpy as np
from pandas_datareader import data | code |
33106742/cell_28 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points = []
larger_number_of_trials = 9001
for i in range(larger_number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points.append(price_series[-1])
expected_ending_price_point = round(np.mean(ending_price_points), 2)
print('Expected Ending Price Point : ', str(expected_ending_price_point)) | code |
33106742/cell_17 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
print('cagr (mean returns) : ', str(round(cagr, 4)))
print('vol (standard deviation of return : )', str(round(vol, 4))) | code |
33106742/cell_31 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = ibm['Adj Close'].pct_change().std()
number_of_trading_days = 252
vol = vol * math.sqrt(number_of_trading_days)
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
number_of_trials = 1000
for i in range(number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points = []
larger_number_of_trials = 9001
for i in range(larger_number_of_trials):
daily_return_percentages = np.random.normal(cagr / number_of_trading_days, vol / math.sqrt(number_of_trading_days), number_of_trading_days) + 1
price_series = [ibm['Adj Close'][-1]]
for drp in daily_return_percentages:
price_series.append(price_series[-1] * drp)
ending_price_points.append(price_series[-1])
expected_ending_price_point = round(np.mean(ending_price_points), 2)
top_ten = np.percentile(ending_price_points, 100 - 10)
bottom_ten = np.percentile(ending_price_points, 10)
plt.hist(ending_price_points, bins=100)
plt.axvline(top_ten, color='r', linestyle='dashed', linewidth=2)
plt.axvline(bottom_ten, color='r', linestyle='dashed', linewidth=2)
plt.axhline(ibm['Adj Close'][-1], color='g', linestyle='dashed', linewidth=2)
plt.show() | code |
33106742/cell_14 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
print(cagr) | code |
2033418/cell_2 | [
"text_plain_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import tensorflow as tf
data = pd.read_csv('../input/TechCrunch.csv', sep=',', error_bad_lines=False, encoding='ISO-8859-1')
data['title'] = data['title'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
data['url'] = data['url'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
def combineProperNouns(a):
y = 0
while y <= len(a) - 2:
if a[y][0].isupper() == True and a[y + 1][0].isupper() == True:
a[y] = str(a[y]) + '+' + str(a[y + 1])
a[y + 1:] = a[y + 2:]
else:
y = y + 1
return a
def recreateDataWithCombinedProperNouns(data):
tempData = []
for x in data.split('.'):
tempPhrase = []
for y in x.split(','):
z = y.split(' ')
z = [a for a in z if len(a) > 0]
tempPhrase.append(' '.join(combineProperNouns(z)))
tempData.append(','.join(tempPhrase))
data = '.'.join(tempData)
return data
def removeDotsFromAcronyms(data):
counter = 0
while counter < len(data) - 2:
if data[counter] == '.' and data[counter + 2] == '.':
data = data[:counter] + str(data[counter + 1]) + ' ' + data[counter + 3:]
counter = counter + 1
elif data[counter] == '.' and data[counter - 1].isupper() == True:
data = data[:counter] + data[counter + 1:]
else:
counter = counter + 1
return data
def stemAndLemmatize(data, columnNames):
wordnet_lemmatizer = WordNetLemmatizer()
porter_stemmer = PorterStemmer()
for columnName in columnNames:
data[columnName] = data[columnName].map(lambda x: ' '.join([porter_stemmer.stem(y) for y in x.split(' ')]))
data[columnName] = data[columnName].map(lambda x: ' '.join([wordnet_lemmatizer.lemmatize(y) for y in x.split(' ')]))
return data
data['newTitle'] = data['title'].map(lambda x: recreateDataWithCombinedProperNouns(x))
data = stemAndLemmatize(data, ['title'])
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] not in ['DT', 'IN', 'PDT', 'TO']]))
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if len(y) > 1]))
tagList = ['NNS', 'NNP', 'NNPS', 'VB', 'VBD', 'VBG']
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] in tagList]))
wordList = set([y for x in data['newTitle'].values for y in x.split(' ')])
print('The number of words are {}'.format(len(wordList)))
vocab_size = len(wordList)
word2int = {}
int2word = {}
for i, word in enumerate(wordList):
word2int[word] = i
int2word[word] = i
words = []
WINDOW_SIZE = 2
for sentence in data['newTitle'].values:
newSentence = sentence.split(' ')
for word_index, word in enumerate(newSentence):
for nb_word in newSentence[max(word_index - WINDOW_SIZE, 0):min(word_index + WINDOW_SIZE, len(newSentence)) + 1]:
if nb_word != word:
words.append([word, nb_word])
def to_one_hot(data_point_index, vocab_size):
temp = np.zeros(vocab_size)
temp[data_point_index] = 1
return temp
x_train = []
y_train = []
for data_word in words:
x_train.append(to_one_hot(word2int[data_word[0]], vocab_size))
y_train.append(to_one_hot(word2int[data_word[1]], vocab_size))
x_train = np.asarray(x_train)
y_train = np.asarray(y_train)
x = tf.placeholder(tf.float32, shape=(None, vocab_size))
y_label = tf.placeholder(tf.float32, shape=(None, vocab_size))
EMBEDDING_DIM = 5
W1 = tf.Variable(tf.random_normal([vocab_size, EMBEDDING_DIM]))
b1 = tf.Variable(tf.random_normal([EMBEDDING_DIM]))
hidden_representation = tf.add(tf.matmul(x, W1), b1)
W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, vocab_size]))
b2 = tf.Variable(tf.random_normal([vocab_size]))
prediction = tf.nn.softmax(tf.add(tf.matmul(hidden_representation, W2), b2)) | code |
2033418/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import pandas as pd
import re
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import tensorflow as tf
data = pd.read_csv('../input/TechCrunch.csv', sep=',', error_bad_lines=False, encoding='ISO-8859-1')
data['title'] = data['title'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
data['url'] = data['url'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
def combineProperNouns(a):
y = 0
while y <= len(a) - 2:
if a[y][0].isupper() == True and a[y + 1][0].isupper() == True:
a[y] = str(a[y]) + '+' + str(a[y + 1])
a[y + 1:] = a[y + 2:]
else:
y = y + 1
return a
def recreateDataWithCombinedProperNouns(data):
tempData = []
for x in data.split('.'):
tempPhrase = []
for y in x.split(','):
z = y.split(' ')
z = [a for a in z if len(a) > 0]
tempPhrase.append(' '.join(combineProperNouns(z)))
tempData.append(','.join(tempPhrase))
data = '.'.join(tempData)
return data
def removeDotsFromAcronyms(data):
counter = 0
while counter < len(data) - 2:
if data[counter] == '.' and data[counter + 2] == '.':
data = data[:counter] + str(data[counter + 1]) + ' ' + data[counter + 3:]
counter = counter + 1
elif data[counter] == '.' and data[counter - 1].isupper() == True:
data = data[:counter] + data[counter + 1:]
else:
counter = counter + 1
return data
def stemAndLemmatize(data, columnNames):
wordnet_lemmatizer = WordNetLemmatizer()
porter_stemmer = PorterStemmer()
for columnName in columnNames:
data[columnName] = data[columnName].map(lambda x: ' '.join([porter_stemmer.stem(y) for y in x.split(' ')]))
data[columnName] = data[columnName].map(lambda x: ' '.join([wordnet_lemmatizer.lemmatize(y) for y in x.split(' ')]))
return data
data['newTitle'] = data['title'].map(lambda x: recreateDataWithCombinedProperNouns(x))
data = stemAndLemmatize(data, ['title'])
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] not in ['DT', 'IN', 'PDT', 'TO']]))
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if len(y) > 1])) | code |
2033418/cell_3 | [
"text_plain_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import tensorflow as tf
data = pd.read_csv('../input/TechCrunch.csv', sep=',', error_bad_lines=False, encoding='ISO-8859-1')
data['title'] = data['title'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
data['url'] = data['url'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
def combineProperNouns(a):
y = 0
while y <= len(a) - 2:
if a[y][0].isupper() == True and a[y + 1][0].isupper() == True:
a[y] = str(a[y]) + '+' + str(a[y + 1])
a[y + 1:] = a[y + 2:]
else:
y = y + 1
return a
def recreateDataWithCombinedProperNouns(data):
tempData = []
for x in data.split('.'):
tempPhrase = []
for y in x.split(','):
z = y.split(' ')
z = [a for a in z if len(a) > 0]
tempPhrase.append(' '.join(combineProperNouns(z)))
tempData.append(','.join(tempPhrase))
data = '.'.join(tempData)
return data
def removeDotsFromAcronyms(data):
counter = 0
while counter < len(data) - 2:
if data[counter] == '.' and data[counter + 2] == '.':
data = data[:counter] + str(data[counter + 1]) + ' ' + data[counter + 3:]
counter = counter + 1
elif data[counter] == '.' and data[counter - 1].isupper() == True:
data = data[:counter] + data[counter + 1:]
else:
counter = counter + 1
return data
def stemAndLemmatize(data, columnNames):
wordnet_lemmatizer = WordNetLemmatizer()
porter_stemmer = PorterStemmer()
for columnName in columnNames:
data[columnName] = data[columnName].map(lambda x: ' '.join([porter_stemmer.stem(y) for y in x.split(' ')]))
data[columnName] = data[columnName].map(lambda x: ' '.join([wordnet_lemmatizer.lemmatize(y) for y in x.split(' ')]))
return data
data['newTitle'] = data['title'].map(lambda x: recreateDataWithCombinedProperNouns(x))
data = stemAndLemmatize(data, ['title'])
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] not in ['DT', 'IN', 'PDT', 'TO']]))
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if len(y) > 1]))
tagList = ['NNS', 'NNP', 'NNPS', 'VB', 'VBD', 'VBG']
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] in tagList]))
wordList = set([y for x in data['newTitle'].values for y in x.split(' ')])
vocab_size = len(wordList)
word2int = {}
int2word = {}
for i, word in enumerate(wordList):
word2int[word] = i
int2word[word] = i
words = []
WINDOW_SIZE = 2
for sentence in data['newTitle'].values:
newSentence = sentence.split(' ')
for word_index, word in enumerate(newSentence):
for nb_word in newSentence[max(word_index - WINDOW_SIZE, 0):min(word_index + WINDOW_SIZE, len(newSentence)) + 1]:
if nb_word != word:
words.append([word, nb_word])
def to_one_hot(data_point_index, vocab_size):
temp = np.zeros(vocab_size)
temp[data_point_index] = 1
return temp
x_train = []
y_train = []
for data_word in words:
x_train.append(to_one_hot(word2int[data_word[0]], vocab_size))
y_train.append(to_one_hot(word2int[data_word[1]], vocab_size))
x_train = np.asarray(x_train)
y_train = np.asarray(y_train)
x = tf.placeholder(tf.float32, shape=(None, vocab_size))
y_label = tf.placeholder(tf.float32, shape=(None, vocab_size))
EMBEDDING_DIM = 5
W1 = tf.Variable(tf.random_normal([vocab_size, EMBEDDING_DIM]))
b1 = tf.Variable(tf.random_normal([EMBEDDING_DIM]))
hidden_representation = tf.add(tf.matmul(x, W1), b1)
W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, vocab_size]))
b2 = tf.Variable(tf.random_normal([vocab_size]))
prediction = tf.nn.softmax(tf.add(tf.matmul(hidden_representation, W2), b2))
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
cross_entropy_loss = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy_loss)
n_iters = 10
print('We will start training now')
for _ in range(n_iters):
sess.run(train_step, feed_dict={x: x_train, y_label: y_train})
print('loss is : ', sess.run(cross_entropy_loss, feed_dict={x: x_train, y_label: y_train})) | code |
2033418/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
from sklearn import preprocessing
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import tensorflow as tf
data = pd.read_csv('../input/TechCrunch.csv', sep=',', error_bad_lines=False, encoding='ISO-8859-1')
data['title'] = data['title'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
data['url'] = data['url'].map(lambda x: re.sub('[^\\x00-\\x7F]+', ' ', x))
def combineProperNouns(a):
y = 0
while y <= len(a) - 2:
if a[y][0].isupper() == True and a[y + 1][0].isupper() == True:
a[y] = str(a[y]) + '+' + str(a[y + 1])
a[y + 1:] = a[y + 2:]
else:
y = y + 1
return a
def recreateDataWithCombinedProperNouns(data):
tempData = []
for x in data.split('.'):
tempPhrase = []
for y in x.split(','):
z = y.split(' ')
z = [a for a in z if len(a) > 0]
tempPhrase.append(' '.join(combineProperNouns(z)))
tempData.append(','.join(tempPhrase))
data = '.'.join(tempData)
return data
def removeDotsFromAcronyms(data):
counter = 0
while counter < len(data) - 2:
if data[counter] == '.' and data[counter + 2] == '.':
data = data[:counter] + str(data[counter + 1]) + ' ' + data[counter + 3:]
counter = counter + 1
elif data[counter] == '.' and data[counter - 1].isupper() == True:
data = data[:counter] + data[counter + 1:]
else:
counter = counter + 1
return data
def stemAndLemmatize(data, columnNames):
wordnet_lemmatizer = WordNetLemmatizer()
porter_stemmer = PorterStemmer()
for columnName in columnNames:
data[columnName] = data[columnName].map(lambda x: ' '.join([porter_stemmer.stem(y) for y in x.split(' ')]))
data[columnName] = data[columnName].map(lambda x: ' '.join([wordnet_lemmatizer.lemmatize(y) for y in x.split(' ')]))
return data
data['newTitle'] = data['title'].map(lambda x: recreateDataWithCombinedProperNouns(x))
data = stemAndLemmatize(data, ['title'])
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] not in ['DT', 'IN', 'PDT', 'TO']]))
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if len(y) > 1]))
tagList = ['NNS', 'NNP', 'NNPS', 'VB', 'VBD', 'VBG']
data['newTitle'] = data['newTitle'].map(lambda x: ' '.join([y for y in x.split(' ') if nltk.pos_tag(y.split())[0][1] in tagList]))
wordList = set([y for x in data['newTitle'].values for y in x.split(' ')])
vocab_size = len(wordList)
word2int = {}
int2word = {}
for i, word in enumerate(wordList):
word2int[word] = i
int2word[word] = i
words = []
WINDOW_SIZE = 2
for sentence in data['newTitle'].values:
newSentence = sentence.split(' ')
for word_index, word in enumerate(newSentence):
for nb_word in newSentence[max(word_index - WINDOW_SIZE, 0):min(word_index + WINDOW_SIZE, len(newSentence)) + 1]:
if nb_word != word:
words.append([word, nb_word])
def to_one_hot(data_point_index, vocab_size):
temp = np.zeros(vocab_size)
temp[data_point_index] = 1
return temp
x_train = []
y_train = []
for data_word in words:
x_train.append(to_one_hot(word2int[data_word[0]], vocab_size))
y_train.append(to_one_hot(word2int[data_word[1]], vocab_size))
x_train = np.asarray(x_train)
y_train = np.asarray(y_train)
x = tf.placeholder(tf.float32, shape=(None, vocab_size))
y_label = tf.placeholder(tf.float32, shape=(None, vocab_size))
EMBEDDING_DIM = 5
W1 = tf.Variable(tf.random_normal([vocab_size, EMBEDDING_DIM]))
b1 = tf.Variable(tf.random_normal([EMBEDDING_DIM]))
hidden_representation = tf.add(tf.matmul(x, W1), b1)
W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, vocab_size]))
b2 = tf.Variable(tf.random_normal([vocab_size]))
prediction = tf.nn.softmax(tf.add(tf.matmul(hidden_representation, W2), b2))
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
cross_entropy_loss = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy_loss)
n_iters = 10
for _ in range(n_iters):
sess.run(train_step, feed_dict={x: x_train, y_label: y_train})
vectors = sess.run(W1 + b1)
from sklearn.manifold import TSNE
model = TSNE(n_components=2, random_state=0)
vectors = model.fit_transform(vectors)
from sklearn import preprocessing
normalizer = preprocessing.Normalizer()
vectors = normalizer.fit_transform(vectors, 'l2')
wordList = list(set([y for x in data['newTitle'].values for y in x.split(' ')]))
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 5))
for word in wordList[0:100]:
ax.annotate(word, (vectors[word2int[word]][0], vectors[word2int[word]][1]))
plt.show()
fig, ax = plt.subplots(figsize=(10, 5))
for word in wordList[100:200]:
ax.annotate(word, (vectors[word2int[word]][0], vectors[word2int[word]][1]))
plt.show() | code |
16147265/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16147265/cell_8 | [
"text_plain_output_1.png"
] | print('End') | code |
16147265/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train_data.csv')
test_df = pd.read_csv('../input/test_data.csv') | code |
121149609/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import torch
import torchvision.transforms as T
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
data_idx = 56
image_path = data_location + '/Images/' + df.iloc[data_idx, 0]
img = mpimg.imread(image_path)
spacy_eng = spacy.load('en')
text = 'This is a good place to find a city'
[token.text.lower() for token in spacy_eng.tokenizer(text)]
class Vocabulary:
def __init__(self, freq_threshold):
self.itos = {0: '<PAD>', 1: '<SOS>', 2: '<EOS>', 3: '<UNK>'}
self.stoi = {v: k for k, v in self.itos.items()}
self.freq_threshold = freq_threshold
def __len__(self):
return len(self.itos)
@staticmethod
def tokenize(text):
return [token.text.lower() for token in spacy_eng.tokenizer(text)]
def build_vocab(self, sentence_list):
frequencies = Counter()
idx = 4
for sentence in sentence_list:
for word in self.tokenize(sentence):
frequencies[word] += 1
if frequencies[word] == self.freq_threshold:
self.stoi[word] = idx
self.itos[idx] = word
idx += 1
def numericalize(self, text):
""" For each word in the text corresponding index token for that word form the vocab built as list """
tokenized_text = self.tokenize(text)
return [self.stoi[token] if token in self.stoi else self.stoi['<UNK>'] for token in tokenized_text]
class FlickrDataset(Dataset):
"""
FlickrDataset
"""
def __init__(self, root_dir, captions_file, transform=None, freq_threshold=5):
self.root_dir = root_dir
self.df = pd.read_csv(caption_file)
self.transform = transform
self.imgs = self.df['image']
self.captions = self.df['caption']
self.vocab = Vocabulary(freq_threshold)
self.vocab.build_vocab(self.captions.tolist())
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
caption = self.captions[idx]
img_name = self.imgs[idx]
img_location = os.path.join(self.root_dir, img_name)
img = Image.open(img_location).convert('RGB')
if self.transform is not None:
img = self.transform(img)
caption_vec = []
caption_vec += [self.vocab.stoi['<SOS>']]
caption_vec += self.vocab.numericalize(caption)
caption_vec += [self.vocab.stoi['<EOS>']]
return (img, torch.tensor(caption_vec))
transforms = T.Compose([T.Resize((224, 224)), T.ToTensor()])
def show_image(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
plt.pause(0.001)
dataset = FlickrDataset(root_dir=data_location + '/Images', captions_file=data_location + '/captions.txt', transform=transforms)
img, caps = dataset[80]
show_image(img, 'Image')
print('Token:', caps)
print('Sentence:')
print([dataset.vocab.itos[token] for token in caps.tolist()]) | code |
121149609/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | v = Vocabulary(freq_threshold=1)
v.build_vocab(['This is a good place to find a city'])
print(v.stoi)
print(v.numericalize('This is a good place to find a city here!!')) | code |
121149609/cell_4 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
data_idx = 56
image_path = data_location + '/Images/' + df.iloc[data_idx, 0]
img = mpimg.imread(image_path)
plt.imshow(img)
plt.show()
for i in range(data_idx, data_idx + 5):
print('Caption:', df.iloc[i, 1]) | code |
121149609/cell_2 | [
"text_plain_output_1.png"
] | #location of the data
data_location = "../input/flickr8k"
!ls $data_location | code |
121149609/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader,Dataset
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import torch
import torch
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
data_idx = 56
image_path = data_location + '/Images/' + df.iloc[data_idx, 0]
img = mpimg.imread(image_path)
spacy_eng = spacy.load('en')
text = 'This is a good place to find a city'
[token.text.lower() for token in spacy_eng.tokenizer(text)]
class Vocabulary:
def __init__(self, freq_threshold):
self.itos = {0: '<PAD>', 1: '<SOS>', 2: '<EOS>', 3: '<UNK>'}
self.stoi = {v: k for k, v in self.itos.items()}
self.freq_threshold = freq_threshold
def __len__(self):
return len(self.itos)
@staticmethod
def tokenize(text):
return [token.text.lower() for token in spacy_eng.tokenizer(text)]
def build_vocab(self, sentence_list):
frequencies = Counter()
idx = 4
for sentence in sentence_list:
for word in self.tokenize(sentence):
frequencies[word] += 1
if frequencies[word] == self.freq_threshold:
self.stoi[word] = idx
self.itos[idx] = word
idx += 1
def numericalize(self, text):
""" For each word in the text corresponding index token for that word form the vocab built as list """
tokenized_text = self.tokenize(text)
return [self.stoi[token] if token in self.stoi else self.stoi['<UNK>'] for token in tokenized_text]
class FlickrDataset(Dataset):
"""
FlickrDataset
"""
def __init__(self, root_dir, captions_file, transform=None, freq_threshold=5):
self.root_dir = root_dir
self.df = pd.read_csv(caption_file)
self.transform = transform
self.imgs = self.df['image']
self.captions = self.df['caption']
self.vocab = Vocabulary(freq_threshold)
self.vocab.build_vocab(self.captions.tolist())
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
caption = self.captions[idx]
img_name = self.imgs[idx]
img_location = os.path.join(self.root_dir, img_name)
img = Image.open(img_location).convert('RGB')
if self.transform is not None:
img = self.transform(img)
caption_vec = []
caption_vec += [self.vocab.stoi['<SOS>']]
caption_vec += self.vocab.numericalize(caption)
caption_vec += [self.vocab.stoi['<EOS>']]
return (img, torch.tensor(caption_vec))
class CapsCollate:
"""
Collate to apply the padding to the captions with dataloader
"""
def __init__(self, pad_idx, batch_first=False):
self.pad_idx = pad_idx
self.batch_first = batch_first
def __call__(self, batch):
imgs = [item[0].unsqueeze(0) for item in batch]
imgs = torch.cat(imgs, dim=0)
targets = [item[1] for item in batch]
targets = pad_sequence(targets, batch_first=self.batch_first, padding_value=self.pad_idx)
return (imgs, targets)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | code |
121149609/cell_7 | [
"text_plain_output_1.png"
] | import spacy
spacy_eng = spacy.load('en')
text = 'This is a good place to find a city'
[token.text.lower() for token in spacy_eng.tokenizer(text)] | code |
121149609/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import torch
import torchvision.transforms as T
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
data_idx = 56
image_path = data_location + '/Images/' + df.iloc[data_idx, 0]
img = mpimg.imread(image_path)
spacy_eng = spacy.load('en')
text = 'This is a good place to find a city'
[token.text.lower() for token in spacy_eng.tokenizer(text)]
class Vocabulary:
def __init__(self, freq_threshold):
self.itos = {0: '<PAD>', 1: '<SOS>', 2: '<EOS>', 3: '<UNK>'}
self.stoi = {v: k for k, v in self.itos.items()}
self.freq_threshold = freq_threshold
def __len__(self):
return len(self.itos)
@staticmethod
def tokenize(text):
return [token.text.lower() for token in spacy_eng.tokenizer(text)]
def build_vocab(self, sentence_list):
frequencies = Counter()
idx = 4
for sentence in sentence_list:
for word in self.tokenize(sentence):
frequencies[word] += 1
if frequencies[word] == self.freq_threshold:
self.stoi[word] = idx
self.itos[idx] = word
idx += 1
def numericalize(self, text):
""" For each word in the text corresponding index token for that word form the vocab built as list """
tokenized_text = self.tokenize(text)
return [self.stoi[token] if token in self.stoi else self.stoi['<UNK>'] for token in tokenized_text]
class FlickrDataset(Dataset):
"""
FlickrDataset
"""
def __init__(self, root_dir, captions_file, transform=None, freq_threshold=5):
self.root_dir = root_dir
self.df = pd.read_csv(caption_file)
self.transform = transform
self.imgs = self.df['image']
self.captions = self.df['caption']
self.vocab = Vocabulary(freq_threshold)
self.vocab.build_vocab(self.captions.tolist())
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
caption = self.captions[idx]
img_name = self.imgs[idx]
img_location = os.path.join(self.root_dir, img_name)
img = Image.open(img_location).convert('RGB')
if self.transform is not None:
img = self.transform(img)
caption_vec = []
caption_vec += [self.vocab.stoi['<SOS>']]
caption_vec += self.vocab.numericalize(caption)
caption_vec += [self.vocab.stoi['<EOS>']]
return (img, torch.tensor(caption_vec))
transforms = T.Compose([T.Resize((224, 224)), T.ToTensor()])
def show_image(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
plt.pause(0.001)
dataset = FlickrDataset(root_dir=data_location + '/Images', captions_file=data_location + '/captions.txt', transform=transforms)
img, caps = dataset[80]
BATCH_SIZE = 4
NUM_WORKER = 1
pad_idx = dataset.vocab.stoi['<PAD>']
data_loader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKER, shuffle=True, collate_fn=CapsCollate(pad_idx=pad_idx, batch_first=True))
dataiter = iter(data_loader)
batch = next(dataiter)
images, captions = batch
for i in range(BATCH_SIZE):
img, cap = (images[i], captions[i])
caption_label = [dataset.vocab.itos[token] for token in cap.tolist()]
eos_index = caption_label.index('<EOS>')
caption_label = caption_label[1:eos_index]
caption_label = ' '.join(caption_label)
show_image(img, caption_label)
plt.show() | code |
121149609/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
print('There are {} image to captions'.format(len(df)))
df.head(7) | code |
105214040/cell_29 | [
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
dt = 0.1
I = np.eye(4)
x = np.matrix([[0.0, 0.0, 0.0, 0.0]]).T
P = np.diag([1000.0, 1000.0, 1000.0, 1000.0])
A = np.matrix([[1.0, 0.0, dt, 0.0], [0.0, 1.0, 0.0, dt], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
H = np.matrix([[0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
r = 100.0
R = np.matrix([[r, 0.0], [0.0, r]])
s = 8.8
G = np.matrix([[0.5 * dt ** 2], [0.5 * dt ** 2], [dt], [dt]])
Q = G * G.T * s ** 2
xt = []
yt = []
dxt = []
dyt = []
Zx = []
Zy = []
Px = []
Py = []
Pdx = []
Pdy = []
Rdx = []
Rdy = []
Kx = []
Ky = []
Kdx = []
Kdy = []
for n in range(len(measurements[0])):
x = A * x
P = A * P * A.T + Q
S = H * P * H.T + R
K = P * H.T * np.linalg.pinv(S)
Z = measurements[:, n].reshape(2, 1)
y = Z - H * x
x = x + K * y
P = (I - K * H) * P
xt.append(float(x[0]))
yt.append(float(x[1]))
dxt.append(float(x[2]))
dyt.append(float(x[3]))
Zx.append(float(Z[0]))
Zy.append(float(Z[1]))
Px.append(float(P[0, 0]))
Py.append(float(P[1, 1]))
Pdx.append(float(P[2, 2]))
Pdy.append(float(P[3, 3]))
Rdx.append(float(R[0, 0]))
Rdy.append(float(R[1, 1]))
Kx.append(float(K[0, 0]))
Ky.append(float(K[1, 0]))
Kdx.append(float(K[2, 0]))
Kdy.append(float(K[3, 0]))
def plot_K():
fig = plt.figure(figsize=(16,9))
plt.plot(range(len(measurements[0])),Kx, label='Kalman Gain for $x$')
plt.plot(range(len(measurements[0])),Ky, label='Kalman Gain for $y$')
plt.plot(range(len(measurements[0])),Kdx, label='Kalman Gain for $\dot x$')
plt.plot(range(len(measurements[0])),Kdy, label='Kalman Gain for $\dot y$')
plt.xlabel('Filter Step')
plt.ylabel('')
plt.title('Kalman Gain (the lower, the more the measurement fullfill the prediction)')
plt.legend(loc='best',prop={'size':22})
plot_K() | code |
105214040/cell_18 | [
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
plt.figure(figsize=(10, 7))
plt.plot(range(m), mx, label='$v_1 (measurements)$')
plt.plot(range(m), my, label='$v_2 (measurements)$')
plt.ylabel('Velocity Measurements')
plt.title('Noisy Measurements')
plt.legend(loc='best', prop={'size': 15})
plt.show() | code |
105214040/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
measurements | code |
105214040/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
dt = 0.1
I = np.eye(4)
x = np.matrix([[0.0, 0.0, 0.0, 0.0]]).T
P = np.diag([1000.0, 1000.0, 1000.0, 1000.0])
A = np.matrix([[1.0, 0.0, dt, 0.0], [0.0, 1.0, 0.0, dt], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
H = np.matrix([[0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
r = 100.0
R = np.matrix([[r, 0.0], [0.0, r]])
s = 8.8
G = np.matrix([[0.5 * dt ** 2], [0.5 * dt ** 2], [dt], [dt]])
Q = G * G.T * s ** 2
xt = []
yt = []
dxt = []
dyt = []
Zx = []
Zy = []
Px = []
Py = []
Pdx = []
Pdy = []
Rdx = []
Rdy = []
Kx = []
Ky = []
Kdx = []
Kdy = []
for n in range(len(measurements[0])):
x = A * x
P = A * P * A.T + Q
S = H * P * H.T + R
K = P * H.T * np.linalg.pinv(S)
Z = measurements[:, n].reshape(2, 1)
y = Z - H * x
x = x + K * y
P = (I - K * H) * P
xt.append(float(x[0]))
yt.append(float(x[1]))
dxt.append(float(x[2]))
dyt.append(float(x[3]))
Zx.append(float(Z[0]))
Zy.append(float(Z[1]))
Px.append(float(P[0, 0]))
Py.append(float(P[1, 1]))
Pdx.append(float(P[2, 2]))
Pdy.append(float(P[3, 3]))
Rdx.append(float(R[0, 0]))
Rdy.append(float(R[1, 1]))
Kx.append(float(K[0, 0]))
Ky.append(float(K[1, 0]))
Kdx.append(float(K[2, 0]))
Kdy.append(float(K[3, 0]))
def plot_K():
fig = plt.figure(figsize=(16,9))
plt.plot(range(len(measurements[0])),Kx, label='Kalman Gain for $x$')
plt.plot(range(len(measurements[0])),Ky, label='Kalman Gain for $y$')
plt.plot(range(len(measurements[0])),Kdx, label='Kalman Gain for $\dot x$')
plt.plot(range(len(measurements[0])),Kdy, label='Kalman Gain for $\dot y$')
plt.xlabel('Filter Step')
plt.ylabel('')
plt.title('Kalman Gain (the lower, the more the measurement fullfill the prediction)')
plt.legend(loc='best',prop={'size':22})
plt.figure(figsize=(10, 7))
plt.plot(range(len(measurements[0])), dxt, label='$v_1$', c='r')
plt.plot(range(len(measurements[0])), dyt, label='$v_2$', c='r')
plt.plot(range(len(measurements[0])), mx, label='$z_1 (measurement)$', c='g')
plt.plot(range(len(measurements[0])), my, label='$z_2 (measurement)$', c='b')
plt.axhline(vx, color='#999999', label='$v_1(real)$')
plt.axhline(vy, color='#999999', label='$v_2(real)$')
plt.title('Estimates of Velocity')
plt.legend(loc='best')
plt.ylim([0, 20])
plt.show()
plt.figure(figsize=(10, 7))
plt.scatter(xt, yt, s=20, label='State', c='black')
plt.scatter(xt[0], yt[0], s=100, label='Start', c='g')
plt.scatter(xt[-1], yt[-1], s=100, label='Goal', c='r')
plt.xlabel('$x_1$')
plt.ylabel('$x_2$')
plt.title('Estimates of Position (Tracking)')
plt.legend(loc='best')
plt.show() | code |
50223492/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
df.head(2) | code |
50223492/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.head() | code |
50223492/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
for items in df.columns[-3:]:
sns.barplot(x=df['gender'], y=df[items])
plt.show() | code |
50223492/cell_19 | [
"image_output_3.png",
"image_output_2.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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
df['total'] = df.math + df.reading + df.writing
df['precent'] = df['total'] / 300 * 100
df.sort_values(by='precent', ascending=False).head(10) | code |
50223492/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50223492/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
df['total'] = df.math + df.reading + df.writing
df['precent'] = df['total'] / 300 * 100
df.head() | code |
50223492/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
df['total'] = df.math + df.reading + df.writing
df['precent'] = df['total'] / 300 * 100
df.sort_values(by='precent', ascending=False).head(10)
passed_ds = df[(df.math > 60) & (df.reading > 60) & (df.writing > 60)]
len(passed_ds)
passedPrecentage = len(passed_ds) / len(df) * 100
passedPrecentage | code |
50223492/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
for items in df.columns[-3:]:
sns.barplot(x=df['race'], y=df[items])
plt.show() | code |
50223492/cell_22 | [
"image_output_3.png",
"image_output_2.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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': 'reading', 'writing score': 'writing'}, inplace=True)
df['total'] = df.math + df.reading + df.writing
df['precent'] = df['total'] / 300 * 100
df.sort_values(by='precent', ascending=False).head(10)
passed_ds = df[(df.math > 60) & (df.reading > 60) & (df.writing > 60)]
len(passed_ds) | code |
50223492/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.info() | code |
105194699/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.head() | code |
105194699/cell_20 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns
null_cols = null_columns.index.tolist()
null_cols
for i in null_cols:
sns.distplot(df[i]) | code |
105194699/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.info() | code |
105194699/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as mno
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns
null_cols = null_columns.index.tolist()
null_cols
import missingno as mno
mno.matrix(df[null_cols], figsize=(20, 6))
plt.show() | code |
105194699/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns
null_cols = null_columns.index.tolist()
print(type(null_cols))
null_cols | code |
105194699/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
sns.countplot(data=df, x=df.Potability)
df.Potability.value_counts() | code |
105194699/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum() | code |
105194699/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns | code |
105194699/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns
null_cols = null_columns.index.tolist()
null_cols
df['ph'] = df['ph'].replace(np.nan, df.ph.mean())
sns.distplot(df.Sulfate) | code |
105194699/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style | code |
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