path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
88087414/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
print(data_train['text'][2])
print(data_train['clean_text'][2])
print(' ')
print(data_test['text'][2])
print(data_test['clean_text'][2]) | code |
88087414/cell_39 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
import keras
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
keras.backend.clear_session()
batch_size = 32
history1 = model1.fit(X_train, Y_train, epochs=10, batch_size=batch_size, validation_data=(X_test, Y_test), verbose=1)
scores = model1.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', round(scores[0], 2))
print('Test accuracy:', round(scores[1], 2)) | code |
88087414/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
print(s)
print('0 :', round(s[0] / len(data_train) * 100, 2), '%')
print('1 :', round(s[1] / len(data_train) * 100, 2), '%')
sns.countplot(data_train['target']) | code |
88087414/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
text = list(data_train[data_train['target'] == 0].text.values)
wordcloud = WordCloud(stopwords=STOPWORDS).generate(str(text))
plt.axis('off')
text = list(data_train[data_train['target'] == 1].text.values)
wordcloud = WordCloud(stopwords=STOPWORDS).generate(str(text))
plt.figure(figsize=(15, 7))
plt.imshow(wordcloud)
plt.axis('off')
plt.title('Wordcloud for Disaster tweets')
plt.show() | code |
88087414/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
from wordcloud import WordCloud,STOPWORDS
import keras
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
text = list(data_train[data_train['target'] == 0].text.values)
wordcloud = WordCloud(stopwords=STOPWORDS).generate(str(text))
plt.axis('off')
text = list(data_train[data_train['target'] == 1].text.values)
wordcloud = WordCloud(stopwords=STOPWORDS).generate(str(text))
plt.axis('off')
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
keras.backend.clear_session()
batch_size = 32
history1 = model1.fit(X_train, Y_train, epochs=10, batch_size=batch_size, validation_data=(X_test, Y_test), verbose=1)
plt.style.use('seaborn')
plt.plot(history1.history['accuracy'])
plt.plot(history1.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show() | code |
88087414/cell_18 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
text = list(data_train[data_train['target'] == 0].text.values)
wordcloud = WordCloud(stopwords=STOPWORDS).generate(str(text))
plt.figure(figsize=(15, 7))
plt.imshow(wordcloud)
plt.axis('off')
plt.title('Wordcloud for normal tweets')
plt.show() | code |
88087414/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
data_train['text_length'].plot.hist() | code |
88087414/cell_16 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
data_train.head() | code |
88087414/cell_38 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
import keras
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
keras.backend.clear_session()
batch_size = 32
history1 = model1.fit(X_train, Y_train, epochs=10, batch_size=batch_size, validation_data=(X_test, Y_test), verbose=1) | code |
88087414/cell_35 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word) | code |
88087414/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
import keras
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
keras.backend.clear_session()
batch_size = 32
history1 = model1.fit(X_train, Y_train, epochs=10, batch_size=batch_size, validation_data=(X_test, Y_test), verbose=1)
scores = model1.evaluate(X_test, Y_test, verbose=0)
predict = model1.predict(X_test)
predict1 = [1 if i > 0.5 else 0 for i in predict]
conf = confusion_matrix(Y_test, predict1)
conf | code |
88087414/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
print('the max length tweet is:', data_train['text_length'].max())
print('the min length tweet is:', data_train['text_length'].min())
print('the avg length tweet is:', data_train['text_length'].mean()) | code |
88087414/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
print('null values for train data : ')
print(data_train.isna().sum())
print('null values for test data : ')
print(data_test.isna().sum()) | code |
88087414/cell_37 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
plot_model(model1, show_shapes=True) | code |
88087414/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train.head() | code |
34121960/cell_6 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
df1.head(3) | code |
34121960/cell_2 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_45.png",
"text_plain_output_14.png",
"text_plain_output_32.png",
"text_plain_output_29.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"text_plain_output_18.png",
"text_plain_output_36.png",
"text_plain_output_3.png",
"text_plain_output_22.png",
"text_plain_output_38.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_41.png",
"text_plain_output_34.png",
"text_plain_output_42.png",
"text_plain_output_23.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"text_plain_output_46.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 |
34121960/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
listOfLists1 = []
with open('../input/CORD-19-research-challenge/json_schema.txt') as f:
for line in f:
inner_list = [line.strip() for line in line.split(' split character')]
listOfLists1.append(inner_list)
df2 = pd.DataFrame(listOfLists1)
df2 | code |
122253082/cell_9 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df['Date'] = pd.to_datetime(df['Date'])
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(df['Date'], df['Average'])
ax.set_title('Tomato Weight Time Series')
ax.set_xlabel('Date')
ax.set_ylabel('Average Weight')
plt.show() | code |
122253082/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df.head() | code |
122253082/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
import plotly.express as px
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
grouped = df.groupby(['Year', 'Month'])['Average'].mean().reset_index()
fig = px.line(grouped, x='Month', y='Average', color='Year', title='Monthly Tomato Price over Years', labels={'Month': 'Month', 'Weight': 'Mean Weight', 'Year': 'Year'})
fig.show() | code |
122253082/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/tomato-daily-prices/Tomato.csv')
backup = df.copy(deep=True)
df.info() | code |
89126605/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from moviepy.editor import *
clip = VideoFileClip('./Rick Astley - Never Gonna Give You Up (Official Music Video).mp4')
clip1 = clip.subclip(0, 5)
clip2 = clip.subclip(60, 65)
final = concatenate_videoclips([clip1, clip2])
final.write_videofile('merged.mp4') | code |
89126605/cell_2 | [
"text_plain_output_1.png"
] | !pip install moviepy
!pip install pytube | code |
89126605/cell_3 | [
"text_plain_output_1.png"
] | import pytube
import pytube
url = 'https://www.youtube.com/watch?v=dQw4w9WgXcQ'
youtube = pytube.YouTube(url)
video = youtube.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()
video.download() | code |
2015996/cell_4 | [
"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)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
cols = ['store_id', 'visit_datetime', 'reserve_datetime', 'reserve_visitors']
air_reserve.columns = cols
hpg_reserve.columns = cols
reserves = pd.DataFrame(columns=cols)
reserves = pd.concat([air_reserve, hpg_reserve])
reserves['visit_datetime'] = pd.to_datetime(reserves['visit_datetime'])
reserves['reserve_datetime'] = pd.to_datetime(reserves['reserve_datetime'])
reserves.info()
reserves.describe() | code |
2015996/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_reserve.head() | code |
2015996/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
hpg_reserve.head() | code |
2015996/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
cols = ['store_id', 'visit_datetime', 'reserve_datetime', 'reserve_visitors']
air_reserve.columns = cols
hpg_reserve.columns = cols
reserves = pd.DataFrame(columns=cols)
reserves = pd.concat([air_reserve, hpg_reserve])
reserves['visit_datetime'] = pd.to_datetime(reserves['visit_datetime'])
reserves['reserve_datetime'] = pd.to_datetime(reserves['reserve_datetime'])
plt.plot_date(x='visit_datetime', y='reserve_visitors', data=reserves) | code |
90120064/cell_21 | [
"image_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
plt.figure(figsize=(9,6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='balance_frequency', y='balance', data=data)
g.set_title('Balance Frequency vs. Balance')
plt.show()
plt.figure(figsize=(9,6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='credit_limit', y='balance', data=data)
g.set_title('Credit Limit vs. Balance')
plt.show()
o_cols = data.select_dtypes(include=['object']).columns.tolist()
num_cols = data.select_dtypes(exclude=['object']).columns.tolist()
ax = plt.axes()
ax.set_facecolor('darkgrey')
data.drop(columns='cust_id', inplace=True)
data.dropna(axis='index', inplace=True)
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
hier_cluster = linkage(data_scaled, method='ward')
plt.figure(figsize=(10, 9))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Observations')
plt.ylabel('Distance')
dendrogram(hier_cluster, truncate_mode='level', p=5, show_leaf_counts=False, no_labels=True)
plt.show() | code |
90120064/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
o_cols = data.select_dtypes(include=['object']).columns.tolist()
num_cols = data.select_dtypes(exclude=['object']).columns.tolist()
data[num_cols].hist(bins=15, figsize=(20, 15), layout=(5, 4)) | code |
90120064/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize': 18}, pad=16) | code |
90120064/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape | code |
90120064/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.describe() | code |
90120064/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
plt.figure(figsize=(9,6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='balance_frequency', y='balance', data=data)
g.set_title('Balance Frequency vs. Balance')
plt.show()
plt.figure(figsize=(9, 6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='credit_limit', y='balance', data=data)
g.set_title('Credit Limit vs. Balance')
plt.show() | code |
90120064/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import warnings
warnings.filterwarnings('ignore') | code |
90120064/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False) | code |
90120064/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.head() | code |
90120064/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
o_cols = data.select_dtypes(include=['object']).columns.tolist()
num_cols = data.select_dtypes(exclude=['object']).columns.tolist()
data.drop(columns='cust_id', inplace=True)
data.head() | code |
90120064/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
plt.figure(figsize=(9,6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='balance_frequency', y='balance', data=data)
g.set_title('Balance Frequency vs. Balance')
plt.show()
plt.figure(figsize=(9,6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='credit_limit', y='balance', data=data)
g.set_title('Credit Limit vs. Balance')
plt.show()
o_cols = data.select_dtypes(include=['object']).columns.tolist()
num_cols = data.select_dtypes(exclude=['object']).columns.tolist()
plt.figure(figsize=(9, 7))
ax = plt.axes()
ax.set_facecolor('darkgrey')
sns.violinplot(x='tenure', y='balance', data=data, inner='quartile')
plt.xlabel('Account Tenure')
plt.ylabel('Account Balance')
plt.title('Account Balance over Tenure')
plt.show() | code |
90120064/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.decomposition import PCA, FactorAnalysis
from scipy.cluster.hierarchy import dendrogram, linkage
import os
import warnings
warnings.filterwarnings('ignore')
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.isnull().sum().sort_values(ascending=False)
plt.figure(figsize=(40, 20))
mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))
heatmap = sns.heatmap(data.corr(), annot=True, mask=mask)
heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);
plt.figure(figsize=(9, 6))
ax = plt.axes()
ax.set_facecolor('darkgrey')
g = sns.scatterplot(x='balance_frequency', y='balance', data=data)
g.set_title('Balance Frequency vs. Balance')
plt.show() | code |
90120064/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '/kaggle/input/ccdata/'
df = pd.read_csv(PATH + 'CC GENERAL.csv')
data = df.copy()
data.columns = data.columns.str.lower()
data.shape
data.info() | code |
90102236/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = ImageDataGenerator(rescale=1 / 255)
test_loader = test_datagen.flow_from_dataframe(dataframe=test, directory=test_path, x_col='filename', batch_size=BATCH_SIZE, shuffle=False, class_mode=None, target_size=(32, 32))
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary()
test_probs = cnn.predict(test_loader)
print(test_probs[:10,].round(2)) | code |
90102236/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = ImageDataGenerator(rescale=1 / 255)
test_loader = test_datagen.flow_from_dataframe(dataframe=test, directory=test_path, x_col='filename', batch_size=BATCH_SIZE, shuffle=False, class_mode=None, target_size=(32, 32)) | code |
90102236/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
print('Test Set Size:', test.shape) | code |
90102236/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test.head() | code |
90102236/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow import keras
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary() | code |
90102236/cell_7 | [
"text_html_output_1.png"
] | import os
test_path = '../input/histopathologic-cancer-detection/test'
print('Test Images:', len(os.listdir(test_path))) | code |
90102236/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
submission = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
submission.head() | code |
90102236/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = ImageDataGenerator(rescale=1 / 255)
test_loader = test_datagen.flow_from_dataframe(dataframe=test, directory=test_path, x_col='filename', batch_size=BATCH_SIZE, shuffle=False, class_mode=None, target_size=(32, 32))
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary()
test_probs = cnn.predict(test_loader)
test_pred = np.argmax(test_probs, axis=1)
submission = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
submission.label = test_pred
submission.head() | code |
90102236/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = ImageDataGenerator(rescale=1 / 255)
test_loader = test_datagen.flow_from_dataframe(dataframe=test, directory=test_path, x_col='filename', batch_size=BATCH_SIZE, shuffle=False, class_mode=None, target_size=(32, 32))
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary()
test_probs = cnn.predict(test_loader)
test_pred = np.argmax(test_probs, axis=1)
print(test_pred[:10]) | code |
90102236/cell_12 | [
"text_html_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd
test = pd.read_csv('../input/histopathologic-cancer-detection/sample_submission.csv')
test['filename'] = test.id + '.tif'
test_path = '../input/histopathologic-cancer-detection/test'
BATCH_SIZE = 64
test_datagen = ImageDataGenerator(rescale=1 / 255)
test_loader = test_datagen.flow_from_dataframe(dataframe=test, directory=test_path, x_col='filename', batch_size=BATCH_SIZE, shuffle=False, class_mode=None, target_size=(32, 32))
cnn = keras.models.load_model('../input/hcd601/HCDv01.h5')
cnn.summary()
test_probs = cnn.predict(test_loader)
print(test_probs.shape) | code |
34144500/cell_21 | [
"text_plain_output_1.png"
] | import json
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
id_to_cat | code |
34144500/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.info() | code |
34144500/cell_33 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.insert(5, 'publish_date', df['publish_time'].dt.date)
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
df.insert(5, 'category', df['category_id'].map(id_to_cat))
df.insert(8, 'publish_to_trend_days', df['trending_date'] - df['publish_date'])
df.insert(7, 'publish_month', df['publish_date'].dt.strftime('%m'))
df.insert(8, 'publish_day', df['publish_date'].dt.strftime('%a'))
df.insert(10, 'publish_hour', df['publish_time'].apply(lambda x: x.hour))
df.head() | code |
34144500/cell_40 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.insert(5, 'publish_date', df['publish_time'].dt.date)
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
df.insert(5, 'category', df['category_id'].map(id_to_cat))
df.insert(8, 'publish_to_trend_days', df['trending_date'] - df['publish_date'])
df.insert(7, 'publish_month', df['publish_date'].dt.strftime('%m'))
df.insert(8, 'publish_day', df['publish_date'].dt.strftime('%a'))
df.insert(10, 'publish_hour', df['publish_time'].apply(lambda x: x.hour))
df_last = df.drop_duplicates(subset=['video_id'], keep='last', inplace=False)
df_first = df.drop_duplicates(subset=['video_id'], keep='first', inplace=False)
print(df['video_id'].duplicated().any())
print(df_last['video_id'].duplicated().any())
print(df_first['video_id'].duplicated().any()) | code |
34144500/cell_39 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.insert(5, 'publish_date', df['publish_time'].dt.date)
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
df.insert(5, 'category', df['category_id'].map(id_to_cat))
df.insert(8, 'publish_to_trend_days', df['trending_date'] - df['publish_date'])
df.insert(7, 'publish_month', df['publish_date'].dt.strftime('%m'))
df.insert(8, 'publish_day', df['publish_date'].dt.strftime('%a'))
df.insert(10, 'publish_hour', df['publish_time'].apply(lambda x: x.hour))
print(df.shape)
df_last = df.drop_duplicates(subset=['video_id'], keep='last', inplace=False)
df_first = df.drop_duplicates(subset=['video_id'], keep='first', inplace=False)
print(df_last.shape)
print(df_first.shape) | code |
34144500/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.describe() | code |
34144500/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.head() | code |
34144500/cell_37 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.insert(5, 'publish_date', df['publish_time'].dt.date)
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
df.insert(5, 'category', df['category_id'].map(id_to_cat))
df.insert(8, 'publish_to_trend_days', df['trending_date'] - df['publish_date'])
df.insert(7, 'publish_month', df['publish_date'].dt.strftime('%m'))
df.insert(8, 'publish_day', df['publish_date'].dt.strftime('%a'))
df.insert(10, 'publish_hour', df['publish_time'].apply(lambda x: x.hour))
len(df['video_id']) | code |
34144500/cell_36 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import json
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import nltk
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', 50)
df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
df.insert(5, 'publish_date', df['publish_time'].dt.date)
id_to_cat = {}
with open('/kaggle/input/youtube-new/US_category_id.json', 'r') as f:
data = json.load(f)
for category in data['items']:
id_to_cat[category['id']] = category['snippet']['title']
df.insert(5, 'category', df['category_id'].map(id_to_cat))
df.insert(8, 'publish_to_trend_days', df['trending_date'] - df['publish_date'])
df.insert(7, 'publish_month', df['publish_date'].dt.strftime('%m'))
df.insert(8, 'publish_day', df['publish_date'].dt.strftime('%a'))
df.insert(10, 'publish_hour', df['publish_time'].apply(lambda x: x.hour))
df['video_id'].nunique() | code |
32062754/cell_4 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')
df_global = pd.read_csv('/kaggle/input/global-hospital-beds-capacity-for-covid19/hospital_beds_global_v1.csv')
df_global.dataframeName = 'hospital_beds_global_v1.csv'
df_global = df_global.merge(df_countries, how='left', left_on=['country'], right_on=['Alpha-2 code'])
df_global.rename(columns={'Alpha-3 code': 'country code', 'English short name lower case': 'country name'}, inplace=True)
df_global = df_global[['country name', 'country code', 'beds', 'type', 'year', 'population']]
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
df_global_acute = df_global[df_global['type'] == 'ACUTE']
mapped = world.merge(df_global_acute[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = 0,df_global_acute['beds'].max()
fig, ax = plt.subplots(1, figsize=(25,25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of ACUTE beds per 1000 inhabitants in countries', fontdict={'fontsize':30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal')
df_global_icu = df_global[df_global['type'] == 'ICU']
mapped = world.merge(df_global_icu[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = (0, df_global_icu['beds'].max())
fig, ax = plt.subplots(1, figsize=(25, 25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of ICU beds per 1000 inhabitants in countries', fontdict={'fontsize': 30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal') | code |
32062754/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')
df_global = pd.read_csv('/kaggle/input/global-hospital-beds-capacity-for-covid19/hospital_beds_global_v1.csv')
df_global.dataframeName = 'hospital_beds_global_v1.csv'
df_global = df_global.merge(df_countries, how='left', left_on=['country'], right_on=['Alpha-2 code'])
df_global.rename(columns={'Alpha-3 code': 'country code', 'English short name lower case': 'country name'}, inplace=True)
df_global = df_global[['country name', 'country code', 'beds', 'type', 'year', 'population']]
df_global.head() | code |
32062754/cell_3 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')
df_global = pd.read_csv('/kaggle/input/global-hospital-beds-capacity-for-covid19/hospital_beds_global_v1.csv')
df_global.dataframeName = 'hospital_beds_global_v1.csv'
df_global = df_global.merge(df_countries, how='left', left_on=['country'], right_on=['Alpha-2 code'])
df_global.rename(columns={'Alpha-3 code': 'country code', 'English short name lower case': 'country name'}, inplace=True)
df_global = df_global[['country name', 'country code', 'beds', 'type', 'year', 'population']]
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
df_global_acute = df_global[df_global['type'] == 'ACUTE']
mapped = world.merge(df_global_acute[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = (0, df_global_acute['beds'].max())
fig, ax = plt.subplots(1, figsize=(25, 25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of ACUTE beds per 1000 inhabitants in countries', fontdict={'fontsize': 30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal') | code |
32062754/cell_5 | [
"image_output_1.png"
] | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
df_countries = pd.read_csv('/kaggle/input/countries-iso-codes/wikipedia-iso-country-codes.csv')
df_global = pd.read_csv('/kaggle/input/global-hospital-beds-capacity-for-covid19/hospital_beds_global_v1.csv')
df_global.dataframeName = 'hospital_beds_global_v1.csv'
df_global = df_global.merge(df_countries, how='left', left_on=['country'], right_on=['Alpha-2 code'])
df_global.rename(columns={'Alpha-3 code': 'country code', 'English short name lower case': 'country name'}, inplace=True)
df_global = df_global[['country name', 'country code', 'beds', 'type', 'year', 'population']]
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
df_global_acute = df_global[df_global['type'] == 'ACUTE']
mapped = world.merge(df_global_acute[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = 0,df_global_acute['beds'].max()
fig, ax = plt.subplots(1, figsize=(25,25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of ACUTE beds per 1000 inhabitants in countries', fontdict={'fontsize':30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal')
df_global_icu = df_global[df_global['type'] == 'ICU']
mapped = world.merge(df_global_icu[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = 0,df_global_icu['beds'].max()
fig, ax = plt.subplots(1, figsize=(25,25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of ICU beds per 1000 inhabitants in countries', fontdict={'fontsize':30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal')
df_global_total = df_global[df_global['type'] == 'TOTAL']
mapped = world.merge(df_global_icu[['country code', 'beds']], how='left', left_on='iso_a3', right_on='country code')
mapped = mapped.fillna(0)
to_be_mapped = 'beds'
vmin, vmax = (0, df_global_total['beds'].max())
fig, ax = plt.subplots(1, figsize=(25, 25))
mapped.plot(column=to_be_mapped, cmap='Blues', linewidth=0.8, ax=ax, edgecolors='0.8')
ax.set_title('Number of TOTAL beds per 1000 inhabitants in countries', fontdict={'fontsize': 30})
ax.set_axis_off()
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm, orientation='horizontal') | code |
18100887/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data) | code |
18100887/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label) | code |
18100887/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d) | code |
18100887/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label) | code |
18100887/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label) | code |
121151851/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Gender'].value_counts() | code |
121151851/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'uk', 'belgium', 'denmark', 'germany', 'mexico'))
states = pd.Series([119, 86, 17, 11, 11, 11, 6, 4, 1, 1], index=('california', 'abroad', 'nevada', 'arizona', 'oregon', 'colorado', 'utah', 'virginia', 'kansas', 'wyoming'))
states.sum() | code |
121151851/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.head() | code |
121151851/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'uk', 'belgium', 'denmark', 'germany', 'mexico'))
print(overseas) | code |
121151851/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
genders = np.array([36, 55, 9])
mylabels = ['Female 36%', 'Male 55%', 'Firm 9%']
myexplode = [0.2, 0, 0]
plt.pie(genders, labels=mylabels, explode=myexplode, shadow=True)
plt.show() | code |
121151851/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360['Country'].unique() | code |
121151851/cell_29 | [
"text_plain_output_1.png"
] | state_cf = state_percent.round() | code |
121151851/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum() | code |
121151851/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360['State'].unique() | code |
121151851/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'uk', 'belgium', 'denmark', 'germany', 'mexico'))
states = pd.Series([119, 86, 17, 11, 11, 11, 6, 4, 1, 1], index=('california', 'abroad', 'nevada', 'arizona', 'oregon', 'colorado', 'utah', 'virginia', 'kansas', 'wyoming'))
states.sum()
states = pd.Series([119, 86, 17, 11, 11, 11, 6, 4, 1, 1], index=('california', 'abroad', 'nevada', 'arizona', 'oregon', 'colorado', 'utah', 'virginia', 'kansas', 'wyoming'))
state_rf = states / 267 * 100
state_rf.round() | code |
121151851/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum() | code |
121151851/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum() | code |
121151851/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100 | code |
121151851/cell_17 | [
"text_plain_output_1.png"
] | print(round(55.38))
print(round(35.89))
print(round(8.71)) | code |
121151851/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['State'].value_counts() | code |
121151851/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Entity'].value_counts() | code |
121151851/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum()
df360.duplicated().sum()
df360['Country'].value_counts() | code |
121151851/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.isnull().sum()
df360.fillna(0, inplace=True)
df360.isnull().sum() | code |
121151851/cell_27 | [
"image_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
rf = pd.Series([108, 70, 17])
rf.sum()
rfp = pd.Series([108, 70, 17], index=['Male', 'Female', 'Firm'])
rfp / 195 * 100
overseas = pd.Series([72, 7, 4, 2, 2, 1, 1, 1], index=('abroad', 'Canada', 'russia', 'uk', 'belgium', 'denmark', 'germany', 'mexico'))
states = pd.Series([119, 86, 17, 11, 11, 11, 6, 4, 1, 1], index=('california', 'abroad', 'nevada', 'arizona', 'oregon', 'colorado', 'utah', 'virginia', 'kansas', 'wyoming'))
states.sum()
states = pd.Series([119, 86, 17, 11, 11, 11, 6, 4, 1, 1], index=('california', 'abroad', 'nevada', 'arizona', 'oregon', 'colorado', 'utah', 'virginia', 'kansas', 'wyoming'))
state_rf = states / 267 * 100
state_rf | code |
121151851/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df360 = pd.read_csv('/kaggle/input/360-real-estate-company/360 real estate company.csv')
df360.info() | code |
122251358/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
import operator
import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_df['prompt'].apply(lambda x: x.lower())
def build_vocab(sentences, verbose=True):
"""
Builds a vocabulary dictionary where keys are the unique words in our sentences and
the values are the word counts.
:param sentences: list of list of words.
:return: dictionary of words and their count.
"""
vocab = {}
for sentence in tqdm(sentences, disable=not verbose):
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def division(n, d):
"""Avoid zero division"""
return n / d if d else -1
def check_coverage(vocab, embeddings_index):
"""
:param vocab: a python dictionary with all the words in our dataframe as keys and their count as value.
:param embeddings_index: a dict-like object where its keys are words and the values are index or the corresponding word's embedding.
"""
a = {}
oov = {}
k = 0
i = 0
for word in tqdm(vocab):
try:
a[word] = embeddings_index[word]
k += vocab[word]
except:
oov[word] = vocab[word]
i += vocab[word]
pass
sorted_x = sorted(oov.items(), key=operator.itemgetter(1))[::-1]
return sorted_x
def count_sentence_match(sentences, embeddings_index):
"""
:param sentences: list of list of words
:return: dictionary of words and their count
"""
sentences_matches = []
for sentence in tqdm(sentences):
match = 0
no_match = 0
for word in sentence:
try:
embeddings_index[word]
match += 1
except KeyError:
no_match += 1
sentences_matches.append(division(match, match + no_match))
return sentences_matches
def count_word_exists(sentences, vocab_dict):
"""
:param sentences: list of list of words
:return: dictionary of words and values
"""
sentences_matches = []
word_appeared = []
for sentence in tqdm(sentences):
exist = 0
word_list = []
for word in sentence:
try:
vocab_dict[word]
exist += 1
word_list.append(word)
except KeyError:
pass
sentences_matches.append(exist)
word_appeared.append(word_list)
return (sentences_matches, word_appeared)
def check_intersection(vocab_input, vocab_tokenizer):
vocab_input = list(vocab_input.keys())
vocab_tokenizer = list(vocab_tokenizer.keys())
intersection = list(set(vocab_input) & set(vocab_tokenizer))
input_percentage = len(intersection) / len(vocab_input)
tokenizer_percentage = len(intersection) / len(vocab_tokenizer)
tqdm.pandas()
sentences = train_df['prompt'].progress_apply(lambda x: x.split()).values
vocab_input = build_vocab(sentences)
print(f'There are {len(vocab_input)} unique words in our vocabulary')
pprint({k: vocab_input[k] for k in list(vocab_input)[:5]}) | code |
122251358/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_df['prompt'].apply(lambda x: x.lower())
print(f'Train shape: {train_df.shape}')
train_df.head() | code |
122251358/cell_15 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
from transformers import AutoTokenizer
import operator
def build_vocab(sentences, verbose=True):
"""
Builds a vocabulary dictionary where keys are the unique words in our sentences and
the values are the word counts.
:param sentences: list of list of words.
:return: dictionary of words and their count.
"""
vocab = {}
for sentence in tqdm(sentences, disable=not verbose):
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def division(n, d):
"""Avoid zero division"""
return n / d if d else -1
def check_coverage(vocab, embeddings_index):
"""
:param vocab: a python dictionary with all the words in our dataframe as keys and their count as value.
:param embeddings_index: a dict-like object where its keys are words and the values are index or the corresponding word's embedding.
"""
a = {}
oov = {}
k = 0
i = 0
for word in tqdm(vocab):
try:
a[word] = embeddings_index[word]
k += vocab[word]
except:
oov[word] = vocab[word]
i += vocab[word]
pass
sorted_x = sorted(oov.items(), key=operator.itemgetter(1))[::-1]
return sorted_x
def count_sentence_match(sentences, embeddings_index):
"""
:param sentences: list of list of words
:return: dictionary of words and their count
"""
sentences_matches = []
for sentence in tqdm(sentences):
match = 0
no_match = 0
for word in sentence:
try:
embeddings_index[word]
match += 1
except KeyError:
no_match += 1
sentences_matches.append(division(match, match + no_match))
return sentences_matches
def count_word_exists(sentences, vocab_dict):
"""
:param sentences: list of list of words
:return: dictionary of words and values
"""
sentences_matches = []
word_appeared = []
for sentence in tqdm(sentences):
exist = 0
word_list = []
for word in sentence:
try:
vocab_dict[word]
exist += 1
word_list.append(word)
except KeyError:
pass
sentences_matches.append(exist)
word_appeared.append(word_list)
return (sentences_matches, word_appeared)
def check_intersection(vocab_input, vocab_tokenizer):
vocab_input = list(vocab_input.keys())
vocab_tokenizer = list(vocab_tokenizer.keys())
intersection = list(set(vocab_input) & set(vocab_tokenizer))
input_percentage = len(intersection) / len(vocab_input)
tokenizer_percentage = len(intersection) / len(vocab_tokenizer)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
vocab_tokenizer = tokenizer.get_vocab()
print(f'There are {len(vocab_tokenizer)} unique words in our vocabulary')
pprint({k: vocab_tokenizer[k] for k in list(vocab_tokenizer)[:5]}) | code |
122251358/cell_17 | [
"text_plain_output_1.png"
] | from pprint import pprint
from tqdm import tqdm
from transformers import AutoTokenizer
import operator
import pandas as pd
train_df = pd.read_parquet(paths.DIFUSSION_DB_META)
train_df.rename(columns={'Prompt': 'prompt'}, inplace=True)
train_df['prompt'] = train_df['prompt'].astype(str)
train_df['prompt'] = train_df['prompt'].apply(lambda x: x.lower())
def build_vocab(sentences, verbose=True):
"""
Builds a vocabulary dictionary where keys are the unique words in our sentences and
the values are the word counts.
:param sentences: list of list of words.
:return: dictionary of words and their count.
"""
vocab = {}
for sentence in tqdm(sentences, disable=not verbose):
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def division(n, d):
"""Avoid zero division"""
return n / d if d else -1
def check_coverage(vocab, embeddings_index):
"""
:param vocab: a python dictionary with all the words in our dataframe as keys and their count as value.
:param embeddings_index: a dict-like object where its keys are words and the values are index or the corresponding word's embedding.
"""
a = {}
oov = {}
k = 0
i = 0
for word in tqdm(vocab):
try:
a[word] = embeddings_index[word]
k += vocab[word]
except:
oov[word] = vocab[word]
i += vocab[word]
pass
sorted_x = sorted(oov.items(), key=operator.itemgetter(1))[::-1]
return sorted_x
def count_sentence_match(sentences, embeddings_index):
"""
:param sentences: list of list of words
:return: dictionary of words and their count
"""
sentences_matches = []
for sentence in tqdm(sentences):
match = 0
no_match = 0
for word in sentence:
try:
embeddings_index[word]
match += 1
except KeyError:
no_match += 1
sentences_matches.append(division(match, match + no_match))
return sentences_matches
def count_word_exists(sentences, vocab_dict):
"""
:param sentences: list of list of words
:return: dictionary of words and values
"""
sentences_matches = []
word_appeared = []
for sentence in tqdm(sentences):
exist = 0
word_list = []
for word in sentence:
try:
vocab_dict[word]
exist += 1
word_list.append(word)
except KeyError:
pass
sentences_matches.append(exist)
word_appeared.append(word_list)
return (sentences_matches, word_appeared)
def check_intersection(vocab_input, vocab_tokenizer):
vocab_input = list(vocab_input.keys())
vocab_tokenizer = list(vocab_tokenizer.keys())
intersection = list(set(vocab_input) & set(vocab_tokenizer))
input_percentage = len(intersection) / len(vocab_input)
tokenizer_percentage = len(intersection) / len(vocab_tokenizer)
tqdm.pandas()
sentences = train_df['prompt'].progress_apply(lambda x: x.split()).values
vocab_input = build_vocab(sentences)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
vocab_tokenizer = tokenizer.get_vocab()
oov = check_coverage(vocab_input, vocab_tokenizer)
sentences_matches = count_sentence_match(sentences_df, vocab_tokenizer)
check_intersection(vocab_input, vocab_tokenizer)
train_df['match'] = sentences_matches | code |
16136181/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
data['visitor_type'].value_counts() | code |
16136181/cell_25 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
def cat_data(i):
pass
for i in cat:
cat_data(i)
from scipy.stats import skew
sns.set()
def continous_data(i):
sns.boxplot(data1[i])
print('--' * 60)
plt.title('Boxplot of ' + str(i))
plt.show()
plt.title('histogram of ' + str(i))
sns.distplot(data1[i], bins=40, kde=True, color='blue')
plt.show()
print('skewness :', skew(data1[i]))
for i in cont:
continous_data(i) | code |
16136181/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.head() | code |
16136181/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
def cat_data(i):
pass
for i in cat:
cat_data(i)
sns.countplot(data.revenue) | code |
16136181/cell_30 | [
"image_output_11.png",
"text_plain_output_5.png",
"image_output_14.png",
"text_plain_output_4.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"image_output_12.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
def cat_data(i):
pass
for i in cat:
cat_data(i)
from scipy.stats import skew
sns.set()
def continous_data(i):
pass
for i in cont:
continous_data(i)
def cat_bivar(i):
pass
for i in cat:
cat_bivar(i)
sns.boxplot(x=data1.revenue, y=data1.avg_exit_rate) | code |
16136181/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
print('Correlation Heat map of the data')
plt.figure(figsize=(15, 10))
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
sns.heatmap(data1[cont].corr(), annot=True, mask=mask, fmt='.2f', vmin=-1, vmax=1)
plt.show() | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.