path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50239477/cell_58 | [
"text_html_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
seed = 27912
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
dataC.sample(5, rando... | code |
50239477/cell_16 | [
"text_html_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
seed = 27912
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
filepathD = '../input/pima-indians-diabetes-database/diabetes.csv'
targetD = 'Outcome'
dataD ... | code |
50239477/cell_47 | [
"text_html_output_1.png"
] | seed = 27912
CX_test.sample(5, random_state=seed) | code |
50239477/cell_35 | [
"text_plain_output_1.png"
] | seed = 27912
Dy.sample(5, random_state=seed) | code |
50239477/cell_43 | [
"text_plain_output_1.png"
] | seed = 27912
Dy_train.sample(5, random_state=seed) | code |
50239477/cell_31 | [
"text_html_output_1.png"
] | seed = 27912
CX.sample(5, random_state=seed) | code |
50239477/cell_46 | [
"text_html_output_1.png"
] | seed = 27912
DX_test.sample(5, random_state=seed) | code |
50239477/cell_27 | [
"text_html_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
seed = 27912
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
filepathD = '../input... | code |
50239477/cell_36 | [
"text_plain_output_1.png"
] | seed = 27912
Ty.sample(5, random_state=seed) | code |
74064119/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
fig, ax = plt.subplots(figsize=(18,9))
plt.title('Valor Gasto por Faixa Etária', fontsize=14, fontweight = 'bold')
frame = plt.gca()
frame.spines['righ... | code |
74064119/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
fig, ax = plt.subplots(figsize=(18,9))
plt.title('Valor Gasto por Faixa Etária', fontsize=14, fontweight = 'bold')
frame = plt.gca()
frame.spines['righ... | code |
74064119/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
df.head() | code |
74064119/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
fig, ax = plt.subplots(figsize=(18,9))
plt.title('Valor Gasto por Faixa Etária', fontsize=14, fontweight = 'bold')
frame = plt.gca()
frame.spines['righ... | code |
74064119/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
fig, ax = plt.subplots(figsize=(18,9))
plt.title('Valor Gasto por Faixa Etária', fontsize=14, fontweight = 'bold')
frame = plt.gca()
frame.spines['righ... | code |
74064119/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/dataviz-facens-20182-ex3/BlackFriday.csv', delimiter=',')
fig, ax = plt.subplots(figsize=(18, 9))
plt.title('Valor Gasto por Faixa Etária', fontsize=14, fontweight='bold')
frame = plt.gca()
frame.spines['right'... | code |
89131938/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89131938/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89131938/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 |
89131938/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89131938/cell_16 | [
"text_html_output_1.png"
] | from matplotlib import pyplot
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
EARTH_RADIUS = 6378.137
def haversine(xy1... | code |
89131938/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89131938/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.head() | code |
89131938/cell_5 | [
"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 = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/train.zip')
test = pd.read_csv('/kaggle/input/nyc-taxi-trip-duration/test.zip')
train.describe() | code |
90153261/cell_4 | [
"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)
class Envierment:
def __init__(self, K, horizon):
self.K, self.horizon = (K, horizon)
self.q_values = list()
for k in range(self.K):
self.... | code |
90153261/cell_11 | [
"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)
### Let's create a simple 'Environment' class and 'Agent' class.
### For every action, the instant reward is sampled from Gaussian distribution with unit standard deviation, each... | code |
90153261/cell_8 | [
"text_html_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)
### Let's create a simple 'Environment' class and 'Agent' class.
### For every action, the instant reward is sampled from Gaussian distribution with unit standard deviation, each... | code |
34132178/cell_21 | [
"image_output_1.png"
] | i = 0
while i != 10:
print('i: ', i)
i += 2
print(i, ' döngü sonunda değerimiz 10') | code |
34132178/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için kullanıyoruz.
data = pd.read_csv('../input/creditcardfraud/creditcard.csv... | code |
34132178/cell_2 | [
"image_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 |
34132178/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için kullanıyoruz.
data = pd.read_csv('../input/creditcardfraud/creditcard.csv... | code |
34132178/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd #Padndas'ı pd olarak import edeceğimizi tanımlıyoruz.
import seaborn as sns # sns'yi de gör... | code |
34132178/cell_7 | [
"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/creditcardfraud/creditcard.csv')
data.corr() | code |
34132178/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd #Padndas'ı pd olarak import edeceğimizi tanımlıyoruz.
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için... | code |
34132178/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için kullanıyoruz.
data = pd.read_csv('../input/creditcardfraud/creditcard.csv... | code |
34132178/cell_3 | [
"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/creditcardfraud/creditcard.csv')
data.head(10) | code |
34132178/cell_14 | [
"image_output_1.png"
] | dictionary = {'elma': 'apple', 'üzüm': 'grape'}
print(dictionary.keys())
print(dictionary.values())
dictionary['elma'] = 'apple1'
print(dictionary)
dictionary['kavun'] = 'melon'
print(dictionary)
del dictionary['elma']
print(dictionary)
print('kavun' in dictionary)
dictionary.clear()
print(dictionary)
del dictionary
pr... | code |
34132178/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd #Padndas'ı pd olarak import edeceğimizi tanımlıyoruz.
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için... | code |
34132178/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için kullanıyoruz.
data = pd.read_csv('../input/creditcardfraud/creditcard.csv... | code |
34132178/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plt ifadesini önceden tanımlamamıştık. Burada onu tanımlıyoruz.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # sns'yi de görselleştirme (visualization tool) için kullanıyoruz.
data = pd.read_csv('../input/creditcardfraud/creditcard.csv... | code |
34132178/cell_5 | [
"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/creditcardfraud/creditcard.csv')
data.info() | code |
129029630/cell_21 | [
"image_output_1.png"
] | from fcmeans import FCM
from fcmeans import FCM
from sklearn.metrics import silhouette_score
import itertools
import itertools
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_... | code |
129029630/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum()
X_numerics = df[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]
... | code |
129029630/cell_6 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3) | code |
129029630/cell_2 | [
"image_output_1.png"
] | pip install fuzzy-c-means | code |
129029630/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum()
print('Data shape is', df.shape) | code |
129029630/cell_19 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from fcmeans import FCM
from sklearn.metrics import silhouette_score
import itertools
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
d... | code |
129029630/cell_7 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum() | code |
129029630/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum()
df.describe() | code |
129029630/cell_16 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from fcmeans import FCM
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', i... | code |
129029630/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum()
print('Is there any missing values', df.isnull().sum().any()) | code |
129029630/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.drop('Gender', inplace=True, axis=1)
df.sample(3)
df.isnull().sum()
X_numerics = df[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]
... | code |
49118528/cell_25 | [
"text_plain_output_1.png"
] | from itertools import product
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/... | code |
49118528/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/competitive-data-science-predict-future-sales/items... | code |
49118528/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/competitive-data-science-predict-future-sales/items.csv')
item_category = pd.read_cs... | code |
49118528/cell_29 | [
"text_html_output_1.png"
] | from itertools import product
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future... | code |
49118528/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/competitive-data-science-predict-future-sales/items... | code |
49118528/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future-sales/test.csv')
items = pd.read_csv('../input/competitive-data-science-predict-future-sales/items... | code |
49118528/cell_27 | [
"text_plain_output_1.png"
] | from itertools import product
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/competitive-data-science-predict-future-sales/sales_train.csv')
test_data = pd.read_csv('../input/competitive-data-science-predict-future... | code |
49118528/cell_5 | [
"text_html_output_1.png"
] | import lightgbm as lgb
import numpy as np
import pandas as pd
for p in [np, pd, lgb]:
print(p.__name__, p.__version__) | code |
1003897/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull... | code |
1003897/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df[... | code |
1003897/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['i... | code |
1003897/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df['test'] = all_df['inter... | code |
1003897/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['i... | code |
1003897/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull... | code |
1003897/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df[... | code |
1003897/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df[... | code |
1003897/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df['test'] = all_df['inter... | code |
1003897/cell_14 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df[... | code |
1003897/cell_22 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull... | code |
1003897/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df[... | code |
1003897/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
INPUT_DIR = '../input/'
TRAIN_FILE = 'train.json'
TEST_FILE = 'test.json'
train_df = pd.read_json(INPUT_DIR + TRAIN_FILE)
test_df = pd.read_json(INPUT_DIR + TEST_FILE)
all_df = pd.concat((train_df, test_df), axis=0)
all_df['train'] = all_df['interest_level'].notnull()
all_df['test'] = all_df['inter... | code |
105193321/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum() | code |
105193321/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape | code |
105193321/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_56 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_55 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_41 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum() | code |
105193321/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.head() | code |
105193321/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
des = airline_data.descri... | code |
105193321/cell_38 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
des = airline_data.descri... | code |
105193321/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
105193321/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.info() | code |
105193321/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
ai... | code |
105193321/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
airline_data = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
data_dic = pd.read_csv('../input/airline-passenger-satisfaction/data_dictionary.csv')
airline_data.shape
airline_data.isnull().sum()
airline_data.duplicated().sum()
de... | code |
17116059/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Revenue'] = df['Price'] * df['Sales']
df.head() | code |
17116059/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
print(quartet) | code |
17116059/cell_16 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Revenue'] = df['Price'] * df['Sales']
df['Price'] = df.Price.str.replace('$', '').r... | code |
17116059/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Revenue'] = df['Price'] * df['Sales']
df['Price'] = df.Price.str.replace('$', '').r... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.