path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104127064/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104127064/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_c... | code |
104127064/cell_31 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ti... | code |
104127064/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104127064/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum() | code |
104127064/cell_27 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=T... | code |
104127064/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e... | code |
104127064/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)
df = pd.read_csv('../input/titanic/train.csv')
df | code |
74050138/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.callbacks import ModelCheckpoint
from keras.layers import Input, BatchNormalization, Activation,Softmax
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import ... | code |
74050138/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
import numpy as np
import os
import seaborn as sns
EPOCHS = 10
BATCH_SIZE = 17
HEIGHT = 256
WIDTH = 256
N_CLASSES = 13
def LoadImage(name, path):
img = Image.open(os.path.join(path, name))
img = np.array(img)
image = img[:, :256]
mask = img[:, 256:]
return (image, mask)
de... | code |
74050138/cell_11 | [
"text_plain_output_1.png"
] | from keras.layers import Input, BatchNormalization, Activation,Softmax
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
EPOCHS = 10
BATCH_SIZE = 17
HEIGHT = 256
WIDTH = 256
N_CLASSE... | code |
74050138/cell_7 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
EPOCHS = 10
BATCH_SIZE = 17
HEIGHT = 256
WIDTH = 256
N_CLASSES = 13
def LoadImage(name, path):
img = Image.open(os.path.join(path, name))
img = np.array(img)
image = img[:, :256]
ma... | code |
74050138/cell_15 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.callbacks import ModelCheckpoint
from keras.layers import Input, BatchNormalization, Activation,Softmax
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import ... | code |
74050138/cell_14 | [
"image_output_1.png"
] | from PIL import Image
from keras.callbacks import ModelCheckpoint
from keras.layers import Input, BatchNormalization, Activation,Softmax
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import ... | code |
74050138/cell_12 | [
"image_output_1.png"
] | from keras.layers import Input, BatchNormalization, Activation,Softmax
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
import tensorflow as tf
EPOCHS = 10
BATCH_SIZE = 17
HEIGHT =... | code |
18153349/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).hea... | code |
18153349/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head() | code |
18153349/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.head() | code |
18153349/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_20 | [
"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 seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns | code |
18153349/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
18153349/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes | code |
18153349/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.describe() | code |
18153349/cell_15 | [
"text_html_output_1.png"
] | dictionary = {'1': 'Istanbul', '2': 'Izmır', '3': 'Ankara', '4': 'London', '5': 'Boston'}
print(dictionary.keys())
print(dictionary.values()) | code |
18153349/cell_16 | [
"text_html_output_1.png"
] | dictionary = {'1': 'Istanbul', '2': 'Izmır', '3': 'Ankara', '4': 'London', '5': 'Boston'}
dictionary['1'] = 'Bursa'
dictionary['6'] = 'İstanbul'
print(dictionary)
del dictionary['3']
print(dictionary)
print('3' in dictionary) | code |
18153349/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.info() | code |
18153349/cell_17 | [
"image_output_1.png"
] | dictionary = {'1': 'Istanbul', '2': 'Izmır', '3': 'Ankara', '4': 'London', '5': 'Boston'}
dictionary.clear()
print(dictionary) | code |
18153349/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
f, ax = plt.subplots(figsize=(20... | code |
18153349/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('../input/pokemon.csv')
data.shape
data.columns
data.dtypes
data.sort_values(by='Attack', ascending=False).head()
#correlation map
f,ax = plt.subp... | code |
18153349/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/pokemon.csv')
data.shape | code |
89133083/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
def breaker(num: int=50, char: str='*') -> None:
pass
def preprocess(image: np.ndarray, size: int) -> np.ndarray:
return cv2.resize(src=cv2.cvtColor(src=image, code=cv2.COLOR_BGR2RGB), dsize=(size, size), interpolation=cv2.INTER_AREA)
def get_image... | code |
89133083/cell_6 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
def breaker(num: int=50, char: str='*') -> None:
pass
def preprocess(image: np.ndarray, size: int) -> np.ndarray:
return cv2.resize(src=cv2.cvtColor(src=image, code=cv2.COLOR_BGR2RGB), dsize=(size, size), interpolation=cv2.INTER_AREA)
def get_image... | code |
89133083/cell_7 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
def breaker(num: int=50, char: str='*') -> None:
pass
def preprocess(image: np.ndarray, size: int) -> np.ndarray:
return cv2.resize(src=cv2.cvtColor(src=image, code=cv2.COLOR_BGR2RGB), dsize=(size, size), interpolation=cv2.INTER_AREA)
def get_image... | code |
89133083/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
def breaker(num: int=50, char: str='*') -> None:
pass
def preprocess(image: np.ndarray, size: int) -> np.ndarray:
return cv2.resize(src=cv2.cvtColor(src=image, code=cv2.COLOR_BGR2RGB), dsize=(size, size), interpolation=cv2.INTER_AREA)
def get_image... | code |
106200991/cell_7 | [
"image_output_1.png"
] | from glob import glob
import matplotlib.pylab as plt
import pandas as pd
train_img = glob('../input/kaggle-pog-series-s01e03/corn/train/*.png')
test_img = glob('../input/kaggle-pog-series-s01e03/corn/test/*.png')
train_df = pd.read_csv('../input/kaggle-pog-series-s01e03/corn/train.csv')
test_df = pd.read_csv('../inp... | code |
106200991/cell_8 | [
"image_output_1.png"
] | from glob import glob
import matplotlib.pylab as plt
import pandas as pd
train_img = glob('../input/kaggle-pog-series-s01e03/corn/train/*.png')
test_img = glob('../input/kaggle-pog-series-s01e03/corn/test/*.png')
train_df = pd.read_csv('../input/kaggle-pog-series-s01e03/corn/train.csv')
test_df = pd.read_csv('../inp... | code |
106200991/cell_10 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
import matplotlib.pylab as plt
import pandas as pd
train_img = glob('../input/kaggle-pog-series-s01e03/corn/train/*.png')
test_img = glob('../input/kaggle-pog-series-s01e03/corn/test/*.png')
train_df = pd.read_csv('../input/kaggle-pog-series-s01e03/corn/train.csv')
test_df = pd.read_csv('../inp... | code |
74045159/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
plt.figure(f... | code |
74045159/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
train_df['date'] = train_df['date'].str.replace('T000000', '')
train_df | code |
74045159/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
slct_test_df = test_df[['date', 'bathrooms', 'sqft_livin... | code |
74045159/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
... | code |
74045159/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
test_df['date'] = test_df['date'].str.replace('T000000',... | code |
74045159/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
slct_test_df = test_df[['date', 'bathrooms', 'sqft_livin... | code |
74045159/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 |
74045159/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
slct_train_... | code |
74045159/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
... | code |
74045159/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
slct_train_... | code |
74045159/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
slct_test_df = test_df[['date', 'bathrooms', 'sqft_livin... | code |
74045159/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df | code |
74045159/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
submit_df = pd.read_csv('/kaggle/input/1056lab-house-pri... | code |
74045159/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
... | code |
74045159/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as s... | code |
74045159/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
slct_train_... | code |
74045159/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
... | code |
74045159/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df | code |
88075597/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod['Age'] = m... | code |
88075597/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod['Age'] = max(df_mod.Dt_Customer.dt.year) - df['Year_Birth']
df_mod = df.rename(columns... | code |
88075597/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-... | code |
88075597/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod['Age'] = m... | code |
88075597/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod['Age'] = max(df_mod.Dt_Customer.dt.year) - df['Year_Birth']
df_mod = df.rename(columns... | code |
88075597/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod['Age'] = max(df_mod.Dt_Customer.dt.year) - df['Year_Birth']
df_mod... | code |
104117681/cell_4 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi | code |
104117681/cell_6 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a) | code |
104117681/cell_11 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000) | code |
104117681/cell_7 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a) | code |
104117681/cell_18 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(5)
math.factorial(5)
l = [1.2, 2.3, 3.4, 4.5]
math.fsum(l) | code |
104117681/cell_8 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a) | code |
104117681/cell_16 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(5) | code |
104117681/cell_3 | [
"text_plain_output_1.png"
] | import math
math.e | code |
104117681/cell_17 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(5)
math.factorial(5) | code |
104117681/cell_14 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree)) | code |
104117681/cell_10 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x) | code |
104117681/cell_12 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = math.pi
math.ceil(a)
math.floor(a)
math.trunc(a)
x = 7
math.exp(x)
math.log(1000)
math.log(1000, 10) | code |
130013718/cell_21 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
130013718/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
130013718/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import numpy as np
from PIL import Image
model = tf.saved_model.load('/kaggle/input/efficientnet-cassava-disease-classification/EfficientNet')
classes = ['Cassava Bacterial Blight (CBB)', 'Cassava Brown Streak Disease (CBSD)', 'Cassava Green Mottle (CGM)', 'Cassava Mosai... | code |
130013718/cell_4 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]... | code |
130013718/cell_23 | [
"text_plain_output_1.png"
] | import os
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _... | code |
130013718/cell_20 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
130013718/cell_6 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
f... | code |
130013718/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]... | code |
130013718/cell_11 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
130013718/cell_19 | [
"text_html_output_1.png"
] | import os
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _... | code |
130013718/cell_1 | [
"text_plain_output_1.png"
] | import os
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5] | code |
130013718/cell_7 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
f... | code |
130013718/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import os
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]
Id = []
import numpy as np
import pandas as pd
i... | code |
130013718/cell_8 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
f... | code |
130013718/cell_15 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
130013718/cell_16 | [
"text_html_output_1.png"
] | from PIL import Image
from sklearn.metrics import classification_report
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. ... | code |
130013718/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]... | code |
130013718/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.metrics import classification_report
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. ... | code |
130013718/cell_14 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
130013718/cell_22 | [
"text_html_output_1.png"
] | import os
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _... | code |
130013718/cell_10 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
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