path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50244608/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(['EmployeeCount', 'StandardHours'... | code |
50244608/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.hist(bins=30, figsize=(20, 20)) | code |
50244608/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes | code |
50244608/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum() | code |
50244608/cell_5 | [
"image_output_1.png"
] | !pip install plotly==4.14.1 | code |
50244608/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(... | code |
17118616/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from nltk.tokenize import RegexpTokenizer
import pandas as pd
import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../input/meta-kaggle/ForumMessages.csv'... | code |
128007844/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col , round
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
df = spark.read.csv('/kaggle/input/students-exam-scores/Original_data_with_more_rows.csv', header=True)
df.createOrReplaceTempView('students_score')
df = df.w... | code |
128007844/cell_1 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
128007844/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
df = spark.read.csv('/kaggle/input/students-exam-scores/Original_data_with_more_rows.csv', header=True)
df_1 = spark.read.csv('/kaggle/input/students-exam-scores/Expanded_data_with_more_features.csv', heade... | code |
128007844/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate() | code |
73072005/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns... | code |
73072005/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
print('Columns in... | code |
73072005/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_valid = ss.transform(X_valid) | code |
73072005/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
test.head() | code |
73072005/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
print('Priyatama is ready!') | code |
73072005/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns... | code |
73072005/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split('... | code |
73072005/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.columns = ['_'.join(col.split(' ')).lower() for col in train.columns]
test.columns = ['_'.join(col.split(' ')).lower() for col in test.columns]
a = train.columns... | code |
73072005/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/breast-cancer-detection/train.csv')
test = pd.read_csv('../input/breast-cancer-detection/test.csv')
train.head() | code |
105199235/cell_11 | [
"text_plain_output_1.png"
] | !ls | code |
105199235/cell_10 | [
"text_plain_output_1.png"
] | !pip wheel --verbose --no-binary cython-bbox==0.1.3 cython-bbox -w /kaggle/working/
!pip wheel --verbose --no-binary lap==0.4.0 lap -w /kaggle/working/
!pip wheel --verbose --no-binary loguru-0.6.0 loguru -w /kaggle/working/
!pip wheel --verbose --no-binary thop-0.1.1.post2209072238 thop -w /kaggle/working/
!git clone ... | code |
105199235/cell_12 | [
"text_plain_output_1.png"
] | !pip install cython_bbox-0.1.3-cp37-cp37m-linux_x86_64.whl
!pip install lap-0.4.0-cp37-cp37m-linux_x86_64.whl
!pip install yolox-0.1.0-cp37-cp37m-linux_x86_64.whl | code |
73096303/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.sh... | code |
73096303/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'].value_counts() | code |
73096303/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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.head() | code |
73096303/cell_23 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.sh... | code |
73096303/cell_6 | [
"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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape | code |
73096303/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.__version__ | code |
73096303/cell_11 | [
"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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
X = data['Review Text'].values
X[:3] | code |
73096303/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.util... | code |
73096303/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import Nor... | code |
73096303/cell_7 | [
"text_plain_output_2.png",
"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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'].isnull().sum() | code |
73096303/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.util... | code |
73096303/cell_8 | [
"text_plain_output_2.png",
"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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Review Text'].str.split().apply(lambda x: len(x)).describ... | code |
73096303/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.sh... | code |
73096303/cell_14 | [
"text_plain_output_1.png"
] | print(X_train.shape, y_train.shape)
print(X_val.shape, y_val.shape)
print(X_test.shape, y_test.shape) | code |
73096303/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
tf.__version__
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
labels = tf.keras.util... | code |
73096303/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/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape | code |
90142380/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | angka = int(input('Masukkan Bilangan Angka = '))
biner = bin(angka).replace('0b', '')
oktal = oct(angka).replace('0o', '')
hexa = hex(angka).replace('0x', '')
print(biner, oktal, hexa) | code |
2043499/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from numba import jit
import math
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gift_pref = pd.read_csv('../input/child_wishlist_v2.csv', header=None).drop(0, 1).values
child_pref = pd.read_csv('../input/gift_goodkids_v2.csv', header=None).drop(0, 1).valu... | code |
2043499/cell_7 | [
"text_plain_output_1.png"
] | '''
INPUT_PATH = '../input/'
def lcm(a, b):
"""Compute the lowest common multiple of a and b"""
# in case of large numbers, using floor division
return a * b // math.gcd(a, b)
#from numba import jit
#@jit(nopython=True)
def avg_normalized_happiness(pred, gift, wish):
n_children = 1000000 # n children to... | code |
16110432/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape | code |
16110432/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/cell_23 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique()
dataset['Gender'].value_counts() | code |
16110432/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16110432/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.head() | code |
16110432/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.info() | code |
16110432/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.nunique() | code |
16110432/cell_24 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
dataset.describe() | code |
16110432/cell_22 | [
"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
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.rename(columns={'Annual Income (k$)': 'Annual_Income', 'Spending Score (1-100)': 'Spending_Score'}, inplace=True)
dataset.shape
... | code |
16110432/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)
dataset = pd.read_csv('../input/Mall_Customers.csv')
dataset.head() | code |
17137806/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()... | code |
17137806/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativ... | code |
17137806/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
17137806/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataF... | code |
17137806/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()... | code |
17137806/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataF... | code |
17137806/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativa = cota[cota['vlrdocumento'] < ... | code |
17137806/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_p... | code |
17137806/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativ... | code |
17137806/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativ... | code |
17137806/cell_16 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativ... | code |
17137806/cell_17 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()
cota_positiva = pd.DataFrame()
cota_negativ... | code |
17137806/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()... | code |
17137806/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
#partido_valor = pd.DataFrame()
cota_negativa = pd.DataFrame()... | code |
17137806/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cota = pd.read_csv('../input/cota_parlamentar_sp.csv', delimiter=';')
cota.dataframeName = 'cota_parlamentar_sp.csv'
nRow, nCol = cota.shape
cota.head(10) | code |
73064668/cell_13 | [
"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/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in trai... | code |
73064668/cell_11 | [
"text_plain_output_1.png"
] | from pandas_profiling import ProfileReport
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
... | code |
73064668/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 |
73064668/cell_7 | [
"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/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in trai... | code |
73064668/cell_8 | [
"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/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in feature]
cont_features = [feature for feature in trai... | code |
73064668/cell_15 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test_df = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
cat_features = [feature for feature in train_df.columns if 'cat' in fea... | code |
105195066/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_26 | [
"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
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/k... | code |
105195066/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/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 |
105195066/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
105195066/cell_27 | [
"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
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/k... | code |
105195066/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
df_test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
sample = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_su... | code |
48163903/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from s... | code |
48163903/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = M... | code |
48163903/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
df_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
df_train.head() | code |
48163903/cell_14 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from s... | code |
48163903/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = M... | code |
48163903/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
scaler = M... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.