path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88091003/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, GroupKFold
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0... | code |
88091003/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install ../input/torchlibrosa/torchlibrosa-0.0.5-py3-none-any.whl > /dev/null | code |
88091003/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, GroupKFold
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0... | code |
88091003/cell_10 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
from sklearn.model_selection import StratifiedKFold, GroupKFold
from tqdm import tqdm
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../inpu... | code |
50237786/cell_21 | [
"text_plain_output_1.png"
] | alphabet = 'abcdefghijklmnopqrstuvwxyz'
key = 'xznlwebgjhqdyvtkfuompciasr'
secret_message = input('Enter your message: ')
secret_message = secret_message.lower()
for c in secret_message:
if c.isalpha():
print(key[alphabet.index(c)], end='')
else:
print(c, end='') | code |
50237786/cell_13 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
s = input('Enter some text: ')
doubled_s = ''
for c in s:
doubled_s = doubled_s + c * 2 | code |
50237786/cell_9 | [
"text_plain_output_1.png"
] | print('\n' * 9) | code |
50237786/cell_11 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
for i in range(len(s)):
if s[i] == 'a':
print(i) | code |
50237786/cell_19 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
s = input('Enter some text: ')
doubled_s = ''
for c in s:
doubled_s = doubled_s + c * 2
s = s.lower()
for... | code |
50237786/cell_1 | [
"text_plain_output_1.png"
] | print('-' * 75) | code |
50237786/cell_7 | [
"text_plain_output_1.png"
] | print('Hi\n\nthere!') | code |
50237786/cell_15 | [
"text_plain_output_1.png"
] | name = input('Enter your name: ')
for i in range(len(name)):
print(name[:i + 1], end=' ') | code |
50237786/cell_3 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
print(s) | code |
50237786/cell_5 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
if s[0].isalpha():
print('Your string starts with a letter')
if not s.isalpha():
print('Your string contains a non-letter.') | code |
128020888/cell_21 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_13 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_25 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'D... | code |
128020888/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})... | code |
128020888/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})... | code |
128020888/cell_11 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_19 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})... | code |
128020888/cell_18 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})... | code |
128020888/cell_15 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_16 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_24 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'D... | code |
128020888/cell_14 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_22 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_10 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '... | code |
128020888/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})... | code |
334146/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_head = train[:10000]
plt.figure(figsize=(20, 15))
plt.scatter(x=train_head.x, y=train_head.y, c=train_head.time) | code |
334146/cell_6 | [
"text_html_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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
334146/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
334146/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
334146/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
plt.figure(figsize=(20, 10))
sns.distplot(bins=200, a=train.accuracy) | code |
334146/cell_3 | [
"text_html_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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
334146/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_head = train[:10000]
plt.figure(figsize=(20, 10))
plt.scatter(x=train_head.time, y=train_head.accuracy) | code |
334146/cell_5 | [
"text_html_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('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
49117600/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
pd.crosstab(data.Department, data.left).plot(kind='bar') | code |
49117600/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
right.shape | code |
49117600/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)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
data.info() | code |
49117600/cell_30 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train)
Reg.predict(X_test) | code |
49117600/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', ... | code |
49117600/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train) | code |
49117600/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean() | code |
49117600/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 |
49117600/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
left.shape | code |
49117600/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', ... | code |
49117600/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', ... | code |
49117600/cell_31 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train)
Reg.predict(X_test)
Reg.score(X_test, y_test) | code |
49117600/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
y = data['left']
y.head() | code |
49117600/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', ... | code |
49117600/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape | code |
49117600/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
pd.crosstab(data.salary, data.left).plot(kind='bar') | code |
49117600/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)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
data.head() | code |
106205052/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
print(e + f) | code |
106205052/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print(c + d) | code |
106205052/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape
b = a[0:3... | code |
106205052/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape
b = a[0:3... | code |
106205052/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print('array1', c)
print('\n')
print('array2', d) | code |
106205052/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape | code |
106205052/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print(c.shape, d.shape) | code |
106205052/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b | code |
106205052/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
print(e.shape)
print(f.shape) | code |
106205052/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
print(a.shape, b.shape)
print(a + b) | code |
129003546/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame() | code |
129003546/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data | code |
129003546/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
country = data['country'].value_counts().head(10)
country.to_frame() | code |
129003546/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes | code |
129003546/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.... | code |
129003546/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
rating = data['rating']
rating.to_frame() | code |
129003546/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
cast = data['cast'].value_counts().head(10)
cast.to_frame() | code |
129003546/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.... | code |
129003546/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.head(2) | code |
129003546/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
director = data['director'].value_counts().head(10)
director.to_frame() | code |
129003546/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum() | code |
129003546/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.... | code |
129003546/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns | code |
129003546/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.... | code |
129003546/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
release_year = data['release_year']
release_year.to_frame() | code |
129003546/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum() | code |
129003546/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes | code |
129003546/cell_36 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.... | code |
2023611/cell_21 | [
"text_plain_output_1.png"
] | from matplotlib import cm
import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
back... | code |
2023611/cell_13 | [
"image_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(... | code |
2023611/cell_6 | [
"image_output_1.png"
] | import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]... | code |
2023611/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import math
import tensorflow as tf
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
import h5py
import cv2
from keras.models import Sequential, load_model, Model
from keras.layers import Input, UpSampling2D
from keras.layers import Dense... | code |
2023611/cell_11 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(... | code |
2023611/cell_7 | [
"image_output_1.png"
] | import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0... | code |
2023611/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from keras.layers import Input, UpSampling2D
from keras.models import Sequential, load_model, Model
def autoencoder():
inputs = Input(shape=(32, 32, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = MaxPooling2D(padding=... | code |
2023611/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from keras.layers import Input, UpSampling2D
from keras.models import Sequential, load_model, Model
from matplotlib import cm
import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.c... | code |
2023611/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]... | code |
2023611/cell_12 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.