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stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
50240297/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df_sex1 = main_df[['Survived', 'Sex']]
main_df_sex1 = main_df_sex1.value_counts().to_frame()
main_df_sex1.reset_index(drop=False, inplace=True)
main_df_sex1.rename(columns={0: 'Counts'}, inplace=True)
main_df_sex1['Survived'] = main_df_sex1['Survived'].replace([0, 1], ['Not-Survived', 'Survived'])
main_df_sex1.set_index(['Survived', 'Sex'], drop=True, inplace=True)
main_df_sex1 | code |
50240297/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
working_df = main_df
working_df.drop(['Name', 'Cabin', 'Ticket'], inplace=True, axis=1)
working_class_df = working_df[['Pclass', 'Survived']]
working_class_df
working_class_df_plot = working_class_df.groupby(['Pclass', 'Survived'])['Pclass'].count().to_frame()
working_class_df_plot.rename(columns={'Pclass': 'Count'}, inplace=True)
working_class_df_plot | code |
50240297/cell_8 | [
"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_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
mean_age = main_df['Age'].mean()
main_df['Age'].replace(np.NaN, mean_age, inplace=True)
print('Column : Age count : ' + str(main_df['Age'].isnull().sum())) | code |
50240297/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df_sex = main_df['Sex'].value_counts()
main_df_sex
main_df_sex = main_df[['Survived', 'Sex']]
main_df_sex_factor = pd.get_dummies(main_df_sex['Sex'])
main_df_sex_factor
main_df_sex['female'] = main_df_sex_factor['female']
main_df_sex['male'] = main_df_sex_factor['male']
main_df_sex.head() | code |
50240297/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df.head() | code |
50240297/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df_sex = main_df['Sex'].value_counts()
main_df_sex
main_df_sex = main_df[['Survived', 'Sex']]
main_df_sex_factor = pd.get_dummies(main_df_sex['Sex'])
main_df_sex_factor
main_df_sex['female'] = main_df_sex_factor['female']
main_df_sex['male'] = main_df_sex_factor['male']
main_df_sex = main_df[['Survived', 'Sex']]
main_df_sex = pd.concat([main_df_sex, pd.get_dummies(main_df['Sex'])], axis=1)
test_df = main_df_sex.groupby(['Survived', 'female', 'male'], as_index=False).count()
test_df | code |
50240297/cell_31 | [
"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)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
working_df = main_df
working_df.drop(['Name', 'Cabin', 'Ticket'], inplace=True, axis=1)
working_class_df = working_df[['Pclass', 'Survived']]
working_class_df
working_class_df_plot = working_class_df.groupby(['Pclass', 'Survived'])['Pclass'].count().to_frame()
working_class_df_plot.rename(columns={'Pclass': 'Count'}, inplace=True)
working_class_df_plot
working_class_df_plot.reset_index(inplace=True)
working_class_df_plot.plot(kind='bar', figsize=(10, 6), color='darkblue')
plt.title('Effect of field Pclass')
plt.xlabel('Pclass')
plt.ylabel('Number of People')
plt.show() | code |
50240297/cell_24 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df | code |
50240297/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df_sex = main_df['Sex'].value_counts()
main_df_sex
main_df_sex = main_df[['Survived', 'Sex']]
main_df_sex_factor = pd.get_dummies(main_df_sex['Sex'])
main_df_sex_factor | code |
50240297/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
main_df_sex1 = main_df[['Survived', 'Sex']]
main_df_sex1 = main_df_sex1.value_counts().to_frame()
main_df_sex1.reset_index(drop=False, inplace=True)
main_df_sex1.rename(columns={0: 'Counts'}, inplace=True)
main_df_sex1['Survived'] = main_df_sex1['Survived'].replace([0, 1], ['Not-Survived', 'Survived'])
main_df_sex1.set_index(['Survived', 'Sex'], drop=True, inplace=True)
main_df_sex1
main_df_sex1.reset_index(drop=False, inplace=True)
for i in main_df_sex1.index:
if main_df_sex1.iloc[i]['Sex'] == 'male':
main_df_sex1.loc[i, '%'] = main_df_sex1.iloc[i]['Counts'] / 577 * 100
if main_df_sex1.iloc[i]['Sex'] == 'female':
main_df_sex1.loc[i, '%'] = main_df_sex1.iloc[i]['Counts'] / 312 * 100
main_df_sex1
male_stats = main_df_sex1[main_df_sex1['Sex'] == 'male']
male_stats.drop(['Counts'], axis=1, inplace=True)
male_stats.set_index(['Survived', 'Sex'], drop=True, inplace=True)
female_stats = main_df_sex1[main_df_sex1['Sex'] == 'female']
female_stats.drop(['Counts'], axis=1, inplace=True)
female_stats.set_index(['Survived', 'Sex'], drop=True, inplace=True)
female_stats | code |
50240297/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
working_df = main_df
working_df.drop(['Name', 'Cabin', 'Ticket'], inplace=True, axis=1)
working_class_df = working_df[['Pclass', 'Survived']]
working_class_df | code |
50240297/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns.to_list()
main_df.drop(main_df[main_df['Embarked'].isnull()].index, inplace=True, axis=0)
print('Column : Embarked count : ' + str(main_df['Embarked'].isnull().sum())) | code |
50240297/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
gender_sub_df.info() | code |
320748/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
numClasses = 10
numEig = 28 * 28
picSize = 28 * 28
trainData = pd.read_csv('../input/train.csv')
testData = pd.read_csv('../input/test.csv')
trainData.sort_values(by=['label'], inplace=True)
trainY = trainData.iloc[:, 0].values
trainX = trainData.iloc[:, 1:].values
testX = testData.iloc[:, :].values
trainMean = np.mean(trainX, axis=0)
trainX = trainX - trainMean
cov = np.cov(trainX.T)
w, v = np.linalg.eig(cov)
ws = np.sort(w)
ws = ws[::-1]
for i in range(0, numEig):
v[:, i] = v[:, np.where(w == ws[i])[0][0]]
v = v[:, :numEig].real
del trainData, testData, cov, w, ws
omega = np.zeros((numClasses, numEig, picSize))
for i in range(0, numClasses):
trainDigit = trainX[np.where(trainY == i)]
print('calculating weights for digit %d, samples %d' % (i, len(trainDigit)))
for k in range(0, len(trainDigit)):
tmp = v.T * trainDigit[k]
omega[i] += tmp
omega[i] /= len(trainDigit) | code |
320748/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
numClasses = 10
numEig = 28 * 28
picSize = 28 * 28
trainData = pd.read_csv('../input/train.csv')
testData = pd.read_csv('../input/test.csv')
trainData.sort_values(by=['label'], inplace=True)
trainY = trainData.iloc[:, 0].values
trainX = trainData.iloc[:, 1:].values
testX = testData.iloc[:, :].values
trainMean = np.mean(trainX, axis=0)
trainX = trainX - trainMean
cov = np.cov(trainX.T)
w, v = np.linalg.eig(cov)
ws = np.sort(w)
ws = ws[::-1]
for i in range(0, numEig):
v[:, i] = v[:, np.where(w == ws[i])[0][0]]
v = v[:, :numEig].real
del trainData, testData, cov, w, ws
omega = np.zeros((numClasses, numEig, picSize))
for i in range(0, numClasses):
trainDigit = trainX[np.where(trainY == i)]
for k in range(0, len(trainDigit)):
tmp = v.T * trainDigit[k]
omega[i] += tmp
omega[i] /= len(trainDigit)
orig = testX[np.random.randint(0, len(testX))]
omega_m = v.T * (orig - trainMean)
dist = np.zeros(numClasses)
for i in range(0, numClasses):
dist[i] = np.linalg.norm(omega[i] - omega_m)
i = dist.argmin()
recon = v.T * omega_m
recon = np.sum(recon, axis=0) + trainMean
match = v.T * omega[i]
match = np.sum(match, axis=0) + trainMean
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True)
ax1.imshow(orig.reshape(28, 28), cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('testX', fontsize=10)
ax2.imshow(recon.reshape(28, 28), cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('reconstruct', fontsize=10)
ax3.imshow(match.reshape(28, 28), cmap=plt.cm.gray)
ax3.axis('off')
ax3.set_title('match', fontsize=10)
plt.show()
plt.close() | code |
33105482/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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df
df.isnull().sum()
df.target | code |
33105482/cell_9 | [
"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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes | code |
33105482/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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
train.head() | code |
33105482/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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test.head() | code |
33105482/cell_19 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df
zero_not_accepted = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
for columns in zero_not_accepted:
df[columns] = df[columns].replace(0, np.NaN)
mean = int(df[columns].mean(skipna=True))
df[columns] = df[columns].replace(np.NaN, mean)
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
y_train.notna()
classifier = KNeighborsClassifier(n_neighbors=97, p=2, metric='euclidean')
classifier.fit(X_train.loc[X_train['target'].notna()][zero_not_accepted], y_train[y_train.notna()]) | code |
33105482/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 |
33105482/cell_7 | [
"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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.head() | code |
33105482/cell_18 | [
"text_plain_output_1.png"
] | y_train.notna() | code |
33105482/cell_8 | [
"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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.describe() | code |
33105482/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df
df.isnull().sum()
df.target
X = df.iloc[:, 0:8]
y = df.iloc[:, 7]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)
y.shape | code |
33105482/cell_17 | [
"text_html_output_1.png"
] | import math
import math
math.sqrt(len(y_test)) | code |
33105482/cell_24 | [
"text_plain_output_1.png"
] | !head /kaggle/working/submit.csv | code |
33105482/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df
zero_not_accepted = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
for columns in zero_not_accepted:
df[columns] = df[columns].replace(0, np.NaN)
mean = int(df[columns].mean(skipna=True))
df[columns] = df[columns].replace(np.NaN, mean)
df.isnull().sum()
df.target
X = df.iloc[:, 0:8]
y = df.iloc[:, 7]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
y_train.notna()
classifier = KNeighborsClassifier(n_neighbors=97, p=2, metric='euclidean')
classifier.fit(X_train.loc[X_train['target'].notna()][zero_not_accepted], y_train[y_train.notna()])
y_predict = classifier.predict_proba(df.loc[df['target'].isna()][zero_not_accepted])
df_submit = pd.DataFrame({'uid': df.loc[df['target'].isna()]['uid'], 'target': y_predict[:, 1]})
df_submit | code |
33105482/cell_10 | [
"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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df | code |
33105482/cell_12 | [
"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('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
test['target'] = np.nan
df = pd.concat([train, test])
df.dtypes
df = df.select_dtypes(exclude=['object'])
df
df.isnull().sum() | code |
33105482/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/train.csv')
test = pd.read_csv('/kaggle/input/ods-mlclass-dubai-2019-03-lecture3-hw/test.csv')
print(train.shape)
print(test.shape) | code |
2024900/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
Converting numbers into words
TO DO
""" | code |
2024900/cell_30 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
"""
Removing accent marks and other diacritics
"""
def remove_accent(tokens):
tokens = [unidecode.unidecode(token) for token in tokens]
return tokens
tokens = remove_accent(tokens)
print('After removing accent markes ', tokens) | code |
2024900/cell_33 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
import re
import re
import string
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
def remove_before_token(sentence, keep_apostrophe=False):
sentence = sentence.strip()
if keep_apostrophe:
PATTERN = '[?|$|&|*|%|@|(|)|~]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
else:
PATTERN = '[^a-zA-Z0-9]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
return filtered_sentence
remove_before_token(text1)
def remove_after_token(tokens):
pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))
filtered_tokens = filter(None, [pattern.sub('', token) for token in tokens])
filtered_text = ' '.join(filtered_tokens)
return filtered_text
remove_special_characters(tokens)
"""
Expanding contraction
"""
CONTRACTION_MAP = {"ain't": 'is not', "aren't": 'are not', "can't": 'cannot', "can't've": 'cannot have', "'cause": 'because', "could've": 'could have', "couldn't": 'could not', "couldn't've": 'could not have', "didn't": 'did not', "doesn't": 'does not', "don't": 'do not', "hadn't": 'had not', "hadn't've": 'had not have', "hasn't": 'has not', "haven't": 'have not', "he'd": 'he would', "he'd've": 'he would have', "he'll": 'he will', "he'll've": 'he he will have', "he's": 'he is', "how'd": 'how did', "how'd'y": 'how do you', "how'll": 'how will', "how's": 'how is', "I'd": 'I would', "I'd've": 'I would have', "I'll": 'I will', "I'll've": 'I will have', "I'm": 'I am', "I've": 'I have', "i'd": 'i would', "i'd've": 'i would have', "i'll": 'i will', "i'll've": 'i will have', "i'm": 'i am', "i've": 'i have', "isn't": 'is not', "it'd": 'it would', "it'd've": 'it would have', "it'll": 'it will', "it'll've": 'it will have', "it's": 'it is', "let's": 'let us', "ma'am": 'madam', "mayn't": 'may not', "might've": 'might have', "mightn't": 'might not', "mightn't've": 'might not have', "must've": 'must have', "mustn't": 'must not', "mustn't've": 'must not have', "needn't": 'need not', "needn't've": 'need not have', "o'clock": 'of the clock', "oughtn't": 'ought not', "oughtn't've": 'ought not have', "shan't": 'shall not', "sha'n't": 'shall not', "shan't've": 'shall not have', "she'd": 'she would', "she'd've": 'she would have', "she'll": 'she will', "she'll've": 'she will have', "she's": 'she is', "should've": 'should have', "shouldn't": 'should not', "shouldn't've": 'should not have', "so've": 'so have', "so's": 'so as', "this's": 'this is', "that'd": 'that would', "that'd've": 'that would have', "that's": 'that is', "there'd": 'there would', "there'd've": 'there would have', "there's": 'there is', "they'd": 'they would', "they'd've": 'they would have', "they'll": 'they will', "they'll've": 'they will have', "they're": 'they are', "they've": 'they have', "to've": 'to have', "wasn't": 'was not', "we'd": 'we would', "we'd've": 'we would have', "we'll": 'we will', "we'll've": 'we will have', "we're": 'we are', "we've": 'we have', "weren't": 'were not', "what'll": 'what will', "what'll've": 'what will have', "what're": 'what are', "what's": 'what is', "what've": 'what have', "when's": 'when is', "when've": 'when have', "where'd": 'where did', "where's": 'where is', "where've": 'where have', "who'll": 'who will', "who'll've": 'who will have', "who's": 'who is', "who've": 'who have', "why's": 'why is', "why've": 'why have', "will've": 'will have', "won't": 'will not', "won't've": 'will not have', "would've": 'would have', "wouldn't": 'would not', "wouldn't've": 'would not have', "y'all": 'you all', "y'all'd": 'you all would', "y'all'd've": 'you all would have', "y'all're": 'you all are', "y'all've": 'you all have', "you'd": 'you would', "you'd've": 'you would have', "you'll": 'you will', "you'll've": 'you will have', "you're": 'you are', "you've": 'you have'}
def expand_contractions(sentence, contraction_mapping):
contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), flags=re.IGNORECASE | re.DOTALL)
def expand_match(contraction):
match = contraction.group(0)
first_char = match[0]
expanded_contraction = contraction_mapping.get(match) if contraction_mapping.get(match) else contraction_mapping.get(match.lower())
expanded_contraction = first_char + expanded_contraction[1:]
return expanded_contraction
expanded_sentence = contractions_pattern.sub(expand_match, sentence)
return expanded_sentence
expanded_corpus = [expand_contractions(txt, CONTRACTION_MAP) for txt in sent_tokenize(text1)]
"""
Method 2 : Peter Norvig sur un seul mot
"""
import re
import nltk
from collections import Counter
def words(text):
return re.findall('\\w+', text.lower())
WORDS = Counter(words(open('../input/big.txt').read()))
def P(word, N=sum(WORDS.values())):
"""Probability of `word`."""
return WORDS[word] / N
def correction(word):
"""Most probable spelling correction for word."""
return max(candidates(word), key=P)
def candidates(word):
"""Generate possible spelling corrections for word."""
return known([word]) or known(edits1(word)) or known(edits2(word)) or [word]
def known(words):
"""The subset of `words` that appear in the dictionary of WORDS."""
return set((w for w in words if w in WORDS))
def edits1(word):
"""All edits that are one edit away from `word`."""
letters = 'abcdefghijklmnopqrstuvwxyz'
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def edits2(word):
"""All edits that are two edits away from `word`."""
return (e2 for e1 in edits1(word) for e2 in edits1(e1))
correction('speling')
correction('fial')
correction('misstkaes') | code |
2024900/cell_20 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
print(tokens) | code |
2024900/cell_11 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
print('With a naive split \n', text1.split(' '))
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
print('\nTokenizing text into words With NLTK \n', tokens)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
print('\nEquivalent method with TreebankWordTokenizer \n', tokenizer.tokenize(text1))
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
print('\nEquivalent method with WordPunctTokenizer \n', tokenizer.tokenize(text1)) | code |
2024900/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
'\nImportation des librairies\n'
import re
import string
import numpy as np
import nltk
from collections import Counter | code |
2024900/cell_7 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
german_text = u'Die Orgellandschaft Südniedersachsen umfasst das Gebiet der Landkreise Goslar, Göttingen, Hameln-Pyrmont, Hildesheim, Holzminden, Northeim und Osterode am Harz sowie die Stadt Salzgitter. Über 70 historische Orgeln vom 17. bis 19. Jahrhundert sind in der südniedersächsischen Orgellandschaft vollständig oder in Teilen erhalten. '
print('\n', sent_tokenize(german_text, language='german'))
print('\n', sent_tokenize(german_text, language='polish')) | code |
2024900/cell_18 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
print(tokens) | code |
2024900/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import brown
"""
Method 1 : Using the brown corpus in NLTK and "in" operator
"""
from nltk.corpus import brown
word_list = brown.words()
len(word_list)
word_set = set(word_list)
'looked' in word_set | code |
2024900/cell_28 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
import string
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
def remove_before_token(sentence, keep_apostrophe=False):
sentence = sentence.strip()
if keep_apostrophe:
PATTERN = '[?|$|&|*|%|@|(|)|~]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
else:
PATTERN = '[^a-zA-Z0-9]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
return filtered_sentence
remove_before_token(text1)
def remove_after_token(tokens):
pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))
filtered_tokens = filter(None, [pattern.sub('', token) for token in tokens])
filtered_text = ' '.join(filtered_tokens)
return filtered_text
remove_special_characters(tokens)
"""
Expanding contraction
"""
CONTRACTION_MAP = {"ain't": 'is not', "aren't": 'are not', "can't": 'cannot', "can't've": 'cannot have', "'cause": 'because', "could've": 'could have', "couldn't": 'could not', "couldn't've": 'could not have', "didn't": 'did not', "doesn't": 'does not', "don't": 'do not', "hadn't": 'had not', "hadn't've": 'had not have', "hasn't": 'has not', "haven't": 'have not', "he'd": 'he would', "he'd've": 'he would have', "he'll": 'he will', "he'll've": 'he he will have', "he's": 'he is', "how'd": 'how did', "how'd'y": 'how do you', "how'll": 'how will', "how's": 'how is', "I'd": 'I would', "I'd've": 'I would have', "I'll": 'I will', "I'll've": 'I will have', "I'm": 'I am', "I've": 'I have', "i'd": 'i would', "i'd've": 'i would have', "i'll": 'i will', "i'll've": 'i will have', "i'm": 'i am', "i've": 'i have', "isn't": 'is not', "it'd": 'it would', "it'd've": 'it would have', "it'll": 'it will', "it'll've": 'it will have', "it's": 'it is', "let's": 'let us', "ma'am": 'madam', "mayn't": 'may not', "might've": 'might have', "mightn't": 'might not', "mightn't've": 'might not have', "must've": 'must have', "mustn't": 'must not', "mustn't've": 'must not have', "needn't": 'need not', "needn't've": 'need not have', "o'clock": 'of the clock', "oughtn't": 'ought not', "oughtn't've": 'ought not have', "shan't": 'shall not', "sha'n't": 'shall not', "shan't've": 'shall not have', "she'd": 'she would', "she'd've": 'she would have', "she'll": 'she will', "she'll've": 'she will have', "she's": 'she is', "should've": 'should have', "shouldn't": 'should not', "shouldn't've": 'should not have', "so've": 'so have', "so's": 'so as', "this's": 'this is', "that'd": 'that would', "that'd've": 'that would have', "that's": 'that is', "there'd": 'there would', "there'd've": 'there would have', "there's": 'there is', "they'd": 'they would', "they'd've": 'they would have', "they'll": 'they will', "they'll've": 'they will have', "they're": 'they are', "they've": 'they have', "to've": 'to have', "wasn't": 'was not', "we'd": 'we would', "we'd've": 'we would have', "we'll": 'we will', "we'll've": 'we will have', "we're": 'we are', "we've": 'we have', "weren't": 'were not', "what'll": 'what will', "what'll've": 'what will have', "what're": 'what are', "what's": 'what is', "what've": 'what have', "when's": 'when is', "when've": 'when have', "where'd": 'where did', "where's": 'where is', "where've": 'where have', "who'll": 'who will', "who'll've": 'who will have', "who's": 'who is', "who've": 'who have', "why's": 'why is', "why've": 'why have', "will've": 'will have', "won't": 'will not', "won't've": 'will not have', "would've": 'would have', "wouldn't": 'would not', "wouldn't've": 'would not have', "y'all": 'you all', "y'all'd": 'you all would', "y'all'd've": 'you all would have', "y'all're": 'you all are', "y'all've": 'you all have', "you'd": 'you would', "you'd've": 'you would have', "you'll": 'you will', "you'll've": 'you will have', "you're": 'you are', "you've": 'you have'}
def expand_contractions(sentence, contraction_mapping):
contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), flags=re.IGNORECASE | re.DOTALL)
def expand_match(contraction):
match = contraction.group(0)
first_char = match[0]
expanded_contraction = contraction_mapping.get(match) if contraction_mapping.get(match) else contraction_mapping.get(match.lower())
expanded_contraction = first_char + expanded_contraction[1:]
return expanded_contraction
expanded_sentence = contractions_pattern.sub(expand_match, sentence)
return expanded_sentence
expanded_corpus = [expand_contractions(txt, CONTRACTION_MAP) for txt in sent_tokenize(text1)]
print('Text before expanding contraction : \n ', text1)
print('\n Text after expanding contraction : \n ', expanded_corpus) | code |
2024900/cell_8 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
print('English token ', tokenizer.tokenize(text1))
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
print('\nFrench token ', french_tokenizer.tokenize("Il fait beau aujourd'hui. Vas-tu sortir ? N'y a-t-il pas du pain ?")) | code |
2024900/cell_16 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
print(tokens) | code |
2024900/cell_3 | [
"text_plain_output_1.png"
] | text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
print(text1) | code |
2024900/cell_24 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
import string
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
def remove_before_token(sentence, keep_apostrophe=False):
sentence = sentence.strip()
if keep_apostrophe:
PATTERN = '[?|$|&|*|%|@|(|)|~]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
else:
PATTERN = '[^a-zA-Z0-9]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
return filtered_sentence
remove_before_token(text1)
def remove_after_token(tokens):
pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))
filtered_tokens = filter(None, [pattern.sub('', token) for token in tokens])
filtered_text = ' '.join(filtered_tokens)
return filtered_text
remove_special_characters(tokens) | code |
2024900/cell_22 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import re
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def remove_before_token(sentence, keep_apostrophe=False):
sentence = sentence.strip()
if keep_apostrophe:
PATTERN = '[?|$|&|*|%|@|(|)|~]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
else:
PATTERN = '[^a-zA-Z0-9]'
filtered_sentence = re.sub(PATTERN, ' ', sentence)
return filtered_sentence
remove_before_token(text1) | code |
2024900/cell_37 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Starting point : tokens
"""
tokens = tokenize_word_text(text1)
"""
Converting all letters to lower or upper case (common : lower case)
"""
def convert_letters(tokens, style='lower'):
if style == 'lower':
tokens = [token.lower() for token in tokens]
else:
tokens = [token.upper() for token in tokens]
return tokens
tokens = convert_letters(tokens)
"""
Remove blancs
"""
def remove_blanc(tokens):
tokens = [token.strip() for token in tokens]
return tokens
tokens = remove_blanc(tokens)
"""
Removing accent marks and other diacritics
"""
def remove_accent(tokens):
tokens = [unidecode.unidecode(token) for token in tokens]
return tokens
tokens = remove_accent(tokens)
"""
Use a stopwords list
"""
stopword_list = nltk.corpus.stopwords.words('english')
' \nCreate your own stopwords list\n'
stopwords = ['a', 'about', 'above', 'across', 'after', 'afterwards']
stopwords += ['again', 'against', 'all', 'almost', 'alone', 'along']
stopwords += ['this', 'is', 'your']
def removeStopwords(wordlist, stopwords):
return [w for w in wordlist if w not in stopwords]
tokens = nltk.word_tokenize(text1)
removeStopwords(tokens, stopwords) | code |
2024900/cell_12 | [
"text_plain_output_1.png"
] | """
from nltk.tokenize import PunktSentenceTokenizer
from nltk.corpus import state_union
train_text = state_union.raw("2005-GWBush.txt")
sample_text = state_union.raw("2006-GWBush.txt")
# train the Punkt tokenizer like:
custom_sent_tokenizer = PunktSentenceTokenizer(train_text)
# we can actually tokenize
tokenized = custom_sent_tokenizer.tokenize(sample_text)
tokenized
""" | code |
2024900/cell_5 | [
"text_plain_output_1.png"
] | from nltk.tokenize import sent_tokenize
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
print('Sentence tokenize in NLTK With sent_tokenize \n', sent_tokenize(text1))
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
print('\nSentence tokenize with PunktSentenceTokenizer \n ', print(sample_sentences)) | code |
2024900/cell_36 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TreebankWordTokenizer
from nltk.tokenize import WordPunctTokenizer
from nltk.tokenize import sent_tokenize
import nltk
import nltk
import nltk
text1 = "ThIs's ã sent tokenize test . this's sent two. is this sent three? sent 4 is cool! Now it's your turn."
"""
Sentence tokenize in NLTK with sent_tokenize
The sent_tokenize function uses an instance of NLTK known as PunktSentenceTokenizer
This instance of NLTK has already been trained to perform tokenization on different European languages on the basis of letters or punctuation that mark the beginning and end of sentences
"""
from nltk.tokenize import sent_tokenize
'\nAutres manières \n'
punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(text1)
""" Autre manière de procéder """
import nltk.data
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
french_tokenizer = nltk.data.load('tokenizers/punkt/french.pickle')
"""
Naive Split
"""
'\nTokenizing text into words\n'
import nltk
tokens = nltk.word_tokenize(text1)
'\nEquivalent method with TreebankWordTokenizer\n'
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
'\nEquivalent method with WordPunctTokenizer \n'
from nltk.tokenize import WordPunctTokenizer
tokenizer = WordPunctTokenizer()
def tokenize_word_text(text):
tokens = nltk.word_tokenize(text)
tokens = [token.strip() for token in tokens]
return tokens
"""
Use a stopwords list
"""
stopword_list = nltk.corpus.stopwords.words('english')
print('StopWords List in English : \n', stopword_list)
' \nCreate your own stopwords list\n'
stopwords = ['a', 'about', 'above', 'across', 'after', 'afterwards']
stopwords += ['again', 'against', 'all', 'almost', 'alone', 'along']
stopwords += ['this', 'is', 'your'] | code |
49129174/cell_13 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
print('p-value is: ', pval)
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
print('chi=%.6f, critical value=%.6f\n' % (chi, critical_value))
if chi > critical_value:
print('At %.2f level of significance, we reject the null hypotheses and accept H1. \nThey are not independent.' % significance)
else:
print('At %.2f level of significance, we accept the null hypotheses. \nThey are independent.' % significance) | code |
49129174/cell_25 | [
"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('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
print('Correlation of Women Entrepreneurship Index with Entrepreneurship Index:', df.corr().iloc[4, 5]) | code |
49129174/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df.info() | code |
49129174/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
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/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
plt.figure(figsize=[10, 15])
sns.barplot(y='Country', x='Female Labor Force Participation Rate', data=df, hue='European Union Membership')
plt.show() | code |
49129174/cell_20 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
sns.relplot(data=df, x='Women Entrepreneurship Index', y='Entrepreneurship Index', hue='European Union Membership', col='Level of development') | code |
49129174/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
df.head() | code |
49129174/cell_29 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
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/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
plt.figure(figsize=[10, 15])
sns.barplot(y='Country', x='Inflation rate', data=df, hue='European Union Membership')
plt.show() | code |
49129174/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
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/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
plt.figure()
sns.scatterplot(data=df, x='Women Entrepreneurship Index', y='Entrepreneurship Index')
sns.regplot(data=df, x='Women Entrepreneurship Index', y='Entrepreneurship Index')
plt.show() | code |
49129174/cell_11 | [
"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/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
print('Correlation of Level of development with European Union Member:', df.corr().iloc[1, 2]) | code |
49129174/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 |
49129174/cell_18 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
sns.relplot(data=df, x='Women Entrepreneurship Index', y='Entrepreneurship Index', hue='European Union Membership') | code |
49129174/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
df.head() | code |
49129174/cell_15 | [
"text_html_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
sns.heatmap(pd.crosstab(df['Level of development'], df['European Union Membership']), annot=True) | code |
49129174/cell_3 | [
"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/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df.head() | code |
49129174/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
from scipy.stats import mannwhitneyu
from scipy.stats import spearmanr
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
from scipy.stats import mannwhitneyu
p = mannwhitneyu(df['Women Entrepreneurship Index'], df['Entrepreneurship Index'])
alpha = 0.05
from scipy.stats import spearmanr
coef, p = spearmanr(df['Women Entrepreneurship Index'], df['Entrepreneurship Index'])
print('Spearmans correlation coefficient: %.3f' % coef)
alpha = 0.05
if p > alpha:
print('Samples are uncorrelated (fail to reject H0) p =', p)
else:
print('Samples are correlated (reject H0) p =', p) | code |
49129174/cell_22 | [
"text_plain_output_1.png"
] | from scipy.stats import chi2
from scipy.stats import chi2_contingency
from scipy.stats import mannwhitneyu
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/women-entrepreneurship-and-labor-force/Dataset3.csv', header=0, sep=';', names=['No', 'Country', 'Level of development', 'European Union Membership', 'Currency', 'Women Entrepreneurship Index', 'Entrepreneurship Index', 'Inflation rate', 'Female Labor Force Participation Rate'])
df['Level of development'] = pd.get_dummies(df['Level of development'], drop_first=True)
df['European Union Membership'] = pd.get_dummies(df['European Union Membership'], drop_first=True)
df['Currency'] = pd.get_dummies(df['Currency'], drop_first=True)
for i in range(len(df)):
if df.iloc[i, 2] == 0:
df.iloc[i, 2] = 1
elif df.iloc[i, 2] == 1:
df.iloc[i, 2] = 0
if df.iloc[i, 3] == 0:
df.iloc[i, 3] = 1
elif df.iloc[i, 3] == 1:
df.iloc[i, 3] = 0
if df.iloc[i, 4] == 0:
df.iloc[i, 4] = 1
elif df.iloc[i, 4] == 1:
df.iloc[i, 4] = 0
from scipy.stats import chi2_contingency
from scipy.stats import chi2
chi, pval, dof, exp = chi2_contingency(pd.crosstab(df['Level of development'], df['European Union Membership']))
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)
from scipy.stats import mannwhitneyu
p = mannwhitneyu(df['Women Entrepreneurship Index'], df['Entrepreneurship Index'])
alpha = 0.05
if p[1] > 1.96 or p[1] < -1.96:
print('There is no diffrenece between the ranks of the two columns p =', p[1])
else:
print('There is diffrenece between the ranks of the two columns p =', p[1]) | code |
89127815/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89127815/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.describe() | code |
89127815/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 |
89127815/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89127815/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
128031511/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
data_train_db.head() | code |
128031511/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
data_train = data_train_db.values
labels = ('Runs', 'Does not run')
sizes = [np.sum(data_train[:, 0]), np.sum(1 - data_train[:, 0])]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.axis('equal')
plt.show() | code |
128031511/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 |
128031511/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
data_train = data_train_db.values
labels = 'Runs', 'Does not run'
sizes = [np.sum(data_train[:,0]), np.sum(1-data_train[:,0])]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
corrMatrix = data_train_db.corr().abs()
plt.figure(figsize=(12, 12))
sn.heatmap(corrMatrix, annot=False)
plt.show()
plt.figure(figsize=(12, 6))
plt.plot(np.arange(1, 100), corrMatrix['Running'][1:100])
plt.title('Correlation with Running')
plt.show() | code |
128031511/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
data_train = data_train_db.values
labels = 'Runs', 'Does not run'
sizes = [np.sum(data_train[:,0]), np.sum(1-data_train[:,0])]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
def myclassifier(data):
if data[1] == 0:
return 0
if data[2] > 1.5 and (data[65] + data[66] < 5 or data[3] + data[4] + data[5] < 4.5 or data[80] + data[81] + data[82] < 4.5) or (data[2] < 1.5 and (data[99] > 3.5 or data[62] + data[63] + data[64] < 5 or data[77] + data[78] + data[79] < 4.5)):
return 0
return 1
err1 = 0
for t in range(data_train.shape[0]):
err1 = err1 + (myclassifier1(data_train[t, :]) != data_train[t, 0])
err2 = 0
for t in range(data_train.shape[0]):
err2 = err2 + (myclassifier(data_train[t, :]) != data_train[t, 0])
print('Train accuracy 1:' + str(1 - err1 / data_train.shape[0]))
print('Train accuracy 2:' + str(1 - err2 / data_train.shape[0]))
print('difference: ' + str(1 - err1 / data_train.shape[0] - 1 + err2 / data_train.shape[0])) | code |
128031511/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
data_train = data_train_db.values
labels = 'Runs', 'Does not run'
sizes = [np.sum(data_train[:,0]), np.sum(1-data_train[:,0])]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
corrMatrix = data_train_db.corr().abs()
data_train_db.columns[79]
print(data_train_db.columns[77])
print(data_train_db.columns[11])
print(data_train_db.columns[24]) | code |
128031511/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
data_train = data_train_db.values
labels = 'Runs', 'Does not run'
sizes = [np.sum(data_train[:,0]), np.sum(1-data_train[:,0])]
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
corrMatrix = data_train_db.corr().abs()
data_train_db.columns[79]
for i in range(2, 99):
data_train_db[data_train_db.columns[i] + ' +'] = data_train_db[data_train_db.columns[i]] + data_train_db[data_train_db.columns[i + 1]]
for i in range(2, 98):
data_train_db[data_train_db.columns[i] + ' ++'] = data_train_db[data_train_db.columns[i]] + data_train_db[data_train_db.columns[i + 1]] + data_train_db[data_train_db.columns[i + 2]]
corrMatrix2 = data_train_db.corr().abs()
S = np.argsort(np.array(corrMatrix['Running']))[::-1]
S = S[1:]
S2 = np.argsort(np.array(corrMatrix2['Running']))[::-1]
S2 = S2[1:]
print(S)
print(S2) | code |
128031511/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train_db = pd.read_csv('/kaggle/input/data-train-db/data_train_db.csv')
data_test_db = pd.read_csv('/kaggle/input/data-test-db/data_test_db.csv')
from scipy import stats
stdev_max = 6
pd.set_option('display.max_rows', data_train_db.shape[0] + 1)
print(data_train_db.columns.get_loc('Controller blanchedalmond'))
print(data_train_db.columns.get_loc('Controller darkgray'))
print(data_train_db.columns.get_loc('Bending of test plate'))
print(data_train_db.columns.get_loc('Second Counterweight'))
print(data_train_db.columns.get_loc('Controller gainsboro')) | code |
16147672/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from xgboost import XGBClassifier
classes = [SVC(), RandomForestClassifier(), AdaBoostClassifier(), BaggingClassifier(), GradientBoostingClassifier(), GaussianNB(), XGBClassifier(), ExtraTreesClassifier()]
params = [[{'kernel': ['rbf'], 'gamma': [0.001, 0.0001], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}], [{'n_estimators': [100, 200, 400], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth': [4, 5, 6, 7, 8], 'criterion': ['gini', 'entropy']}], [{'n_estimators': [100, 200, 300, 400, 500], 'learning_rate': [0.01, 0.1, 1]}], [{'n_estimators': [100, 200, 300, 400, 500], 'max_samples': [0.5, 0.75, 1.0]}], [{'loss': ['deviance'], 'n_estimators': [50, 100, 300, 400, 500], 'max_depth': [3, 5, 8]}], [{'priors': [None]}], [{'learning_rate': [0.03, 0.05], 'max_depth': [1, 2, 4, 6, 8, 10], 'n_estimators': [50, 100, 300, 500]}], [{'n_estimators': [100, 200, 400, 500], 'criterion': ['gini', 'entropy'], 'max_depth': [1, 2, 4, 6, 8, 10]}]]
'params1=[[ {\'n_estimators\': [100,200,400],\n\'learning_rate\':[0.01,0.1,1]}],[{\'n_estimators\': [100,200,400],\n\'max_samples\':[.5, .75, 1.0]}] ,[{"loss":["deviance"],"n_estimators":[50,100,300],\n"max_depth":[3,5,8]}] ,[{"priors":[None]}],[{\'learning_rate\':[.03, .05],\'max_depth\': [1,2,4,6,8,10],\'n_estimators\':[10, 50, 100, 300]} ],\n [{\'n_estimators\':[100,200,400],\'criterion\':[\'gini\', \'entropy\'],\'max_depth\':[1,2,4,6,8,10]}]]\n'
testlist = []
paramslist = []
bestscore = []
bestestimator = []
for c, p in zip(classes, params):
print('the model using is {}'.format(c))
print('\n')
print('\n')
grid_search = GridSearchCV(estimator=c, param_grid=p, cv=5, n_jobs=-1)
grid_search.fit(train_x, train_y.values.ravel())
best_param = grid_search.best_params_
print(best_param)
paramslist.append(best_param)
best_score = grid_search.best_score_
bestscore.append(best_score)
best_estimator = grid_search.best_estimator_
bestestimator.append(best_estimator)
ypred = best_estimator.predict(test_x)
testlist.append(tuple((accuracy_score(test_y, ypred), f1_score(test_y, ypred)))) | code |
16147672/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16147672/cell_7 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
for col in data1:
if data1[col].isnull().any():
lis.append(col)
for col in data2:
if data2[col].isnull().any():
lis1.append(col)
lis.remove('Cabin')
lis1.remove('Cabin')
data1['Age'].fillna(data1['Age'].median(), inplace=True)
data1['Embarked'].fillna(data1['Embarked'].mode()[0], inplace=True)
data2['Age'].fillna(data2['Age'].median(), inplace=True)
data1['Fare'].fillna(data1['Fare'].median(), inplace=True)
data2['Fare'].fillna(data2['Fare'].median(), inplace=True)
drop_val = ['PassengerId', 'Cabin', 'Ticket']
data1.drop(drop_val, axis=1, inplace=True)
for dataset in frame:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['IsAlone'] = 1
dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0
label = preprocessing.LabelEncoder()
for d in frame:
d['Sex'] = label.fit_transform(d['Sex'])
d['Embarked'] = label.fit_transform(d['Embarked'])
tar = ['Survived']
x_label = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilySize', 'IsAlone']
data1.drop(['Name'], axis=1, inplace=True)
data1.isnull().sum() | code |
16147672/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
type(data_test) | code |
16147672/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
for col in data1:
if data1[col].isnull().any():
lis.append(col)
for col in data2:
if data2[col].isnull().any():
lis1.append(col)
lis.remove('Cabin')
lis1.remove('Cabin')
data1['Age'].fillna(data1['Age'].median(), inplace=True)
data1['Embarked'].fillna(data1['Embarked'].mode()[0], inplace=True)
data2['Age'].fillna(data2['Age'].median(), inplace=True)
data1['Fare'].fillna(data1['Fare'].median(), inplace=True)
data2['Fare'].fillna(data2['Fare'].median(), inplace=True)
drop_val = ['PassengerId', 'Cabin', 'Ticket']
data1.drop(drop_val, axis=1, inplace=True)
for dataset in frame:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['IsAlone'] = 1
dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0
label = preprocessing.LabelEncoder()
for d in frame:
d['Sex'] = label.fit_transform(d['Sex'])
d['Embarked'] = label.fit_transform(d['Embarked'])
tar = ['Survived']
x_label = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilySize', 'IsAlone']
data1.drop(['Name'], axis=1, inplace=True)
data1.isnull().sum()
data1 | code |
16147672/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from xgboost import XGBClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
for col in data1:
if data1[col].isnull().any():
lis.append(col)
for col in data2:
if data2[col].isnull().any():
lis1.append(col)
lis.remove('Cabin')
lis1.remove('Cabin')
data1['Age'].fillna(data1['Age'].median(), inplace=True)
data1['Embarked'].fillna(data1['Embarked'].mode()[0], inplace=True)
data2['Age'].fillna(data2['Age'].median(), inplace=True)
data1['Fare'].fillna(data1['Fare'].median(), inplace=True)
data2['Fare'].fillna(data2['Fare'].median(), inplace=True)
drop_val = ['PassengerId', 'Cabin', 'Ticket']
data1.drop(drop_val, axis=1, inplace=True)
for dataset in frame:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['IsAlone'] = 1
dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0
label = preprocessing.LabelEncoder()
for d in frame:
d['Sex'] = label.fit_transform(d['Sex'])
d['Embarked'] = label.fit_transform(d['Embarked'])
tar = ['Survived']
x_label = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilySize', 'IsAlone']
data1.drop(['Name'], axis=1, inplace=True)
classes = [SVC(), RandomForestClassifier(), AdaBoostClassifier(), BaggingClassifier(), GradientBoostingClassifier(), GaussianNB(), XGBClassifier(), ExtraTreesClassifier()]
params = [[{'kernel': ['rbf'], 'gamma': [0.001, 0.0001], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}], [{'n_estimators': [100, 200, 400], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth': [4, 5, 6, 7, 8], 'criterion': ['gini', 'entropy']}], [{'n_estimators': [100, 200, 300, 400, 500], 'learning_rate': [0.01, 0.1, 1]}], [{'n_estimators': [100, 200, 300, 400, 500], 'max_samples': [0.5, 0.75, 1.0]}], [{'loss': ['deviance'], 'n_estimators': [50, 100, 300, 400, 500], 'max_depth': [3, 5, 8]}], [{'priors': [None]}], [{'learning_rate': [0.03, 0.05], 'max_depth': [1, 2, 4, 6, 8, 10], 'n_estimators': [50, 100, 300, 500]}], [{'n_estimators': [100, 200, 400, 500], 'criterion': ['gini', 'entropy'], 'max_depth': [1, 2, 4, 6, 8, 10]}]]
'params1=[[ {\'n_estimators\': [100,200,400],\n\'learning_rate\':[0.01,0.1,1]}],[{\'n_estimators\': [100,200,400],\n\'max_samples\':[.5, .75, 1.0]}] ,[{"loss":["deviance"],"n_estimators":[50,100,300],\n"max_depth":[3,5,8]}] ,[{"priors":[None]}],[{\'learning_rate\':[.03, .05],\'max_depth\': [1,2,4,6,8,10],\'n_estimators\':[10, 50, 100, 300]} ],\n [{\'n_estimators\':[100,200,400],\'criterion\':[\'gini\', \'entropy\'],\'max_depth\':[1,2,4,6,8,10]}]]\n'
testlist = []
paramslist = []
bestscore = []
bestestimator = []
for c, p in zip(classes, params):
grid_search = GridSearchCV(estimator=c, param_grid=p, cv=5, n_jobs=-1)
grid_search.fit(train_x, train_y.values.ravel())
best_param = grid_search.best_params_
paramslist.append(best_param)
best_score = grid_search.best_score_
bestscore.append(best_score)
best_estimator = grid_search.best_estimator_
bestestimator.append(best_estimator)
ypred = best_estimator.predict(test_x)
testlist.append(tuple((accuracy_score(test_y, ypred), f1_score(test_y, ypred))))
data3 = data2.copy(deep=True)
data3.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
data3.drop(['PassengerId'], axis=1, inplace=True)
yy = bestestimator[6].predict(data3)
submission = pd.DataFrame({'PassengerId': data2['PassengerId'], 'Survived': yy})
submission.head()
filename = 'Titanic Predictions 2.csv'
submission.to_csv(filename, index=False)
print('Saved file: ' + filename) | code |
16147672/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data1 = data_train.copy(deep=True)
data2 = data_test.copy(deep=True)
frame = [data1, data2]
lis = []
lis1 = []
print(data1.info())
print(data2.info())
for col in data1:
if data1[col].isnull().any():
lis.append(col)
for col in data2:
if data2[col].isnull().any():
lis1.append(col)
lis.remove('Cabin')
lis1.remove('Cabin')
data1['Age'].fillna(data1['Age'].median(), inplace=True)
data1['Embarked'].fillna(data1['Embarked'].mode()[0], inplace=True)
data2['Age'].fillna(data2['Age'].median(), inplace=True)
data1['Fare'].fillna(data1['Fare'].median(), inplace=True)
data2['Fare'].fillna(data2['Fare'].median(), inplace=True)
drop_val = ['PassengerId', 'Cabin', 'Ticket']
data1.drop(drop_val, axis=1, inplace=True)
for dataset in frame:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['IsAlone'] = 1
dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0
label = preprocessing.LabelEncoder()
for d in frame:
d['Sex'] = label.fit_transform(d['Sex'])
d['Embarked'] = label.fit_transform(d['Embarked'])
tar = ['Survived']
x_label = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilySize', 'IsAlone']
data1.drop(['Name'], axis=1, inplace=True) | code |
1005486/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/epi_r.csv')
sorted(list(df.columns))
df.describe() | code |
1005486/cell_2 | [
"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/epi_r.csv')
df.head() | code |
1005486/cell_3 | [
"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/epi_r.csv')
sorted(list(df.columns)) | code |
1005486/cell_5 | [
"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)
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
df.pivot_table(index=['rating'], columns=['fat'], aggfunc=np.mean) | code |
89131571/cell_13 | [
"text_html_output_1.png"
] | import csv
import pandas as pd
import sqlite3
path = './'
database = path + 'ted-talk-data.sqlite'
conn = sqlite3.connect(database)
create_table = 'CREATE TABLE tedtalk(\n title TEXT,\n author TEXT,\n date DATE,\n views INTEGER,\n likes INTEGER,\n link TEXT);\n '
cursor = conn.cursor()
cursor.execute(create_table)
file = open('../input/ted-talks/data.csv')
content = list(csv.reader(file))
content = content[1:]
file.close()
sql_test = pd.read_sql('SELECT * FROM tedtalk LIMIT 5', conn)
sql_test | code |
17141744/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
learn = language_model_learner(data, AWD_LSTM, drop_mult=0.3)
learn.lr_find()
learn.fit_one_cycle(10, 0.01) | code |
17141744/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
data.show_batch() | code |
17141744/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/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
train.head() | code |
17141744/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
test_datalist = TextList.from_df(test, cols='Phrase', vocab=data.vocab)
data_clas = TextList.from_df(train, cols='Phrase', vocab=data.vocab).split_by_rand_pct(0.2).label_from_df(cols='Sentiment', classes=['1', '2', '3', '4', '0'], label_cls=CategoryList).add_test(test_datalist).databunch(bs=32)
learn_classifier = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)
learn_classifier.load_encoder('fine_tuned_enc')
learn_classifier.freeze()
learn_classifier.lr_find()
learn_classifier.fit_one_cycle(10, 0.01)
learn_classifier.show_results() | code |
17141744/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
learn = language_model_learner(data, AWD_LSTM, drop_mult=0.3)
learn.lr_find() | code |
17141744/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
test_datalist = TextList.from_df(test, cols='Phrase', vocab=data.vocab)
data_clas = TextList.from_df(train, cols='Phrase', vocab=data.vocab).split_by_rand_pct(0.2).label_from_df(cols='Sentiment', classes=['1', '2', '3', '4', '0'], label_cls=CategoryList).add_test(test_datalist).databunch(bs=32)
learn_classifier = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)
learn_classifier.load_encoder('fine_tuned_enc')
learn_classifier.freeze()
learn_classifier.lr_find()
learn_classifier.fit_one_cycle(10, 0.01) | code |
17141744/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17141744/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
test['Phrase'][0] | code |
17141744/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(bs=48)
test_datalist = TextList.from_df(test, cols='Phrase', vocab=data.vocab)
data_clas = TextList.from_df(train, cols='Phrase', vocab=data.vocab).split_by_rand_pct(0.2).label_from_df(cols='Sentiment', classes=['1', '2', '3', '4', '0'], label_cls=CategoryList).add_test(test_datalist).databunch(bs=32)
learn_classifier = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)
learn_classifier.load_encoder('fine_tuned_enc')
learn_classifier.freeze()
learn_classifier.lr_find()
learn_classifier.recorder.plot() | code |
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