path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128022704/cell_12 | [
"text_plain_output_1.png"
] | data = deque_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop() | code |
128022704/cell_5 | [
"text_plain_output_1.png"
] | usual_list = []
for i in range(ELEMENTS_LIMIT):
usual_list.append(i) | code |
34144956/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
images = train.iloc[:, 1:].values.reshape(train.shape[0], 28, 28).astype... | code |
34144956/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
test.head() | code |
34144956/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ReduceLROnPlateau,ModelCheckpoint
from tensorflow.keras.layers import Dense,Conv2D, MaxPooling2D, Flatten,BatchNormalization,Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorica... | code |
34144956/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
images = train.iloc[:, 1:].values.reshape(train.shape[0], 28, 28).astype... | code |
34144956/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
images = train.iloc[:, 1:].values.reshape(train.shape[0], 28, 28).astype... | code |
34144956/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 |
34144956/cell_18 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ReduceLROnPlateau,ModelCheckpoint
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
earlystop = EarlyStopping(patience=10)
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.5, min_lr=1e-05)
callbacks = [... | code |
34144956/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
images = train.iloc[:, 1:].values.reshape(train.shape[0], 28, 28).astype... | code |
34144956/cell_15 | [
"text_html_output_1.png"
] | from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
label_train = to_categorical(label_train)
label_validation = to_categorical(label_validation)
(label_train, label_validation) | code |
34144956/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
train.head() | code |
34144956/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Dense,Conv2D, MaxPooling2D, Flatten,BatchNormalization,Dropout
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1), padding='same', activation='relu'))
model.add(BatchNormalization(momentum=0.9, epsilon=1e-05, gam... | code |
34144956/cell_24 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ReduceLROnPlateau,ModelCheckpoint
from tensorflow.keras.layers import Dense,Conv2D, MaxPooling2D, Flatten,BatchNormalization,Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorica... | code |
34144956/cell_12 | [
"text_html_output_1.png"
] | label_train | code |
34144956/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')
sample_submission = pd.read_csv('/kaggle/input/Kannada-MNIST/sample_submission.csv')
sample_submission.he... | code |
33111161/cell_9 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'aga... | code |
33111161/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'against_grass', 'agains... | code |
33111161/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'again... | code |
33111161/cell_2 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import os
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import preprocessing
init_notebook_mode()
from plotly.subplots import make_subplots
import plotly.express as px
import plotly.graph_objects as go
from... | code |
33111161/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'aga... | code |
33111161/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'aga... | code |
33111161/cell_8 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'aga... | code |
33111161/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'aga... | code |
33111161/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/'
pokemon = pd.read_csv(filepath + 'complete-pokemon-dataset-updated-090420/pokedex_(Update.04.20).csv').drop('Unnamed: 0', axis=1)
columns_to_drop = ['japanese_name', 'german_name', 'against_normal', 'against_fire', 'against_water', 'against_electric', 'against_grass', 'agains... | code |
33106099/cell_13 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_path + input_data_file)
rep = {'text': '', 'cite_spans': '', 'ref_spans': '', 'section': '', '... | code |
33106099/cell_8 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_path + input_data_file)
rep = {'text': '', 'cite_spans': '', 'ref_spans': '', 'section': '', '... | code |
33106099/cell_16 | [
"text_plain_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_path + input_data_file)
rep = {'text': '', 'cite_spans': ... | code |
33106099/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_pa... | code |
33106099/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_path + input_data_file)
rep = {'text': '', 'cite_spans': '', 'ref_spans': '', 'section': '', '... | code |
33106099/cell_12 | [
"text_html_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
input_data_path = '/kaggle/input/'
input_data_file = 'filt_merged_text_vector_df_200430.csv'
input_data = pd.read_csv(input_data_path + input_data_file)
rep = {'text': '', 'cite_spans': '', 'ref_spans': '', 'section': '', '... | code |
129023760/cell_21 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df = df.dro... | code |
129023760/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train) | code |
129023760/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
y_predlogit = logreg.predict(X_test)
accuracylogit = accuracy_score(y_test, y_predlogit)
print('Accuracy:', roun... | code |
129023760/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df = df.dro... | code |
129023760/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 |
129023760/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
print('Shap... | code |
129023760/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df = df.dro... | code |
129023760/cell_16 | [
"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 warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df = df.dro... | code |
129023760/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None) | code |
129023760/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df = df.dro... | code |
129023760/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_test)
from sklearn.metrics import accuracy_score
accurac... | code |
129023760/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd, numpy as np, seaborn as sns, matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/titanic/train.csv')
df.head() | code |
104121998/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int) | code |
104121998/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
(b[0], b[2], b[-1]) | code |
104121998/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a[1:3] | code |
104121998/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
d | code |
104121998/cell_56 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
c.dtype | code |
104121998/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_44 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float) | code |
104121998/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape | code |
104121998/cell_39 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
d.dtype | code |
104121998/cell_65 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_48 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_61 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a.dtype
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A... | code |
104121998/cell_54 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_67 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a[::2] | code |
104121998/cell_60 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a.dtype
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A... | code |
104121998/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
b.dtype | code |
104121998/cell_69 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_50 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_52 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_64 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
(a[0], a[1]) | code |
104121998/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_49 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
b | code |
104121998/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_51 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_68 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_62 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a.dtype
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A... | code |
104121998/cell_59 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a.dtype
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A... | code |
104121998/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a[0:] | code |
104121998/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a | code |
104121998/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_47 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_66 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
A.shape
A... | code |
104121998/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a.dtype | code |
104121998/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
b[[0, 2, -1]] | code |
104121998/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
a[1:-1] | code |
104121998/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
np.array([1, 2, 3, 4], dtype=float)
np.array([1, 2, 3, 4], dtype=int)
c = np.array(['a', 'b', 'c'])
d = np.array([{'a': 1}])
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.arr... | code |
104121998/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4])
a = np.array([1, 2, 3, 4])
b = np.array([0, 0.5, 1, 1.5, 2])
b | code |
104121998/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
np.array([1, 2, 3, 4]) | code |
106202407/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('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
106202407/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
106202407/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/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train.head() | code |
106202407/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('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
106202407/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 |
106202407/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
train_X.info() | code |
106202407/cell_8 | [
"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/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
106202407/cell_15 | [
"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/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
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