path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2025290/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = hous... | code |
2025290/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfnum['LotFrontage'].fillna(housedfnum['LotFrontage'].mean(), inplace=True)
housedfnum['MasVnrArea'].fil... | code |
2025290/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv') | code |
2025290/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu... | code |
2025290/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum() | code |
2025290/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = hous... | code |
2025290/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu... | code |
2025290/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object]) | code |
329572/cell_23 | [
"text_html_output_1.png"
] | c_ids = [df.Cliente_ID.values[int(i)] for i in np.linspace(0, len(df) - 1, 100)]
var_list, p_var_list = (get_vars(c_ids), get_vars(c_ids, percent=True)) | code |
329572/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
df.Demanda_uni_equil.hist(bins=100, log=True)
plt.xlabel('D... | code |
329572/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
demand_sorted = df.Demanda_uni_equil.sort_values(ascending=... | code |
329572/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
types = {'Semana': np.uint8, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint32}
df = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)
df.Demanda_uni_equil.hist(bins=100)
plt.xlabel('Demand per ... | code |
2026814/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id')
train_band_1 = np.array([np.array(band).astype(np.float32).reshape((7... | code |
2026814/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import tensorflow as tf
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from tqdm import tqdm | code |
2026814/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id')
train_band_1 = np.array([np.array(ba... | code |
2026814/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id')
train_band_1 = np.ar... | code |
2026814/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from tqdm import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submis... | code |
2026814/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id')
train_band_1 = np.ar... | code |
2026814/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import tensorflow as tf
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('i... | code |
2026814/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id') | code |
2026814/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from tqdm import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submis... | code |
2026814/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
data_path = './data'
train = pd.read_json(data_path + '/' + 'train.json')
test = pd.read_json(data_path + '/' + 'test.json')
submission = pd.read_csv(data_path + '/' + 'sample_submission.csv').set_index('id')
train_band_1 = np.array([np.array(band).astype(np.float32).reshape((7... | code |
105180335/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('The data in Groceries is:', groceries.values)
print('The index of Groceries is:', groceries.index) | code |
105180335/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import numpy as np
print('Original grocery list of fruits:\n', fruits)
print()
print('EXP(X)... | code |
105180335/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
print('Original grocery list of fruits:\n ', fruits)
print()
print('fruits + 2:\n', fruits + 2)
print()
print('f... | code |
105180335/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('Original Grocery List:\n', groceries)
groceries.drop('apples', inplace=True)
print()
print('Grocery List after removing apples in place:\n', groceries) | code |
105180335/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
print('Original grocery list of fruits:\n ', fruits)
print()
print('Amount of bananas + 2 = ', fruits['bananas']... | code |
105180335/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('The data in Groceries is:', groceries.values)
print('checking if we have eggs in the groceies:', 'egg' in groceries)
print('checking if we have bananas in the groceies:', 'bananas' in groce... | code |
105180335/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_45 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('Original Grocery List:\n', groceries)
print()
print('We remove apples (out of place):\n', groceries.drop('apples'))
print()
print('Grocery List after removing apples out of place:\n', groce... | code |
105180335/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_51 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('Groceries has shape:', groceries.shape)
print('Groceries has dimension:', groceries.ndim)
print('Groceries has a total of', groceries.size, 'elements') | code |
105180335/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('Original Grocery List:\n', groceries)
groceries['eggs'] = 2
print()
print('Modified Grocery List:\n', groceries) | code |
105180335/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
print('How many eggs do we need to buy:', groceries['eggs'])
print()
print('Do we need milk and bread:\n', groceries[['milk', 'bread']])
print()
print('How many eggs and apples do we need to buy:\... | code |
105180335/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits | code |
105180335/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
105180335/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries | code |
105180335/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
groceries = pd.Series(data=[20, 6, 'Yes', 'No'], index=['eggs', 'apples', 'milk', 'bread'])
groceries
fruits = pd.Series(data=[10, 6, 3], index=['apples', 'oranges', 'bananas'])
fruits
import pandas as pd
items = {'Bob': pd.Series(data=[245, 25, 55], index=['bike', 'pants', '... | code |
128045003/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre
top_... | code |
128045003/cell_9 | [
"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"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
ds.describe() | code |
128045003/cell_4 | [
"image_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.info() | code |
128045003/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.c... | code |
128045003/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns | code |
128045003/cell_2 | [
"image_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds | code |
128045003/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre
top_... | code |
128045003/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns) | code |
128045003/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.c... | code |
128045003/cell_8 | [
"image_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
ds.head() | code |
128045003/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre
top_dancable_songs = ds[['danceabilit... | code |
128045003/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.c... | code |
128045003/cell_3 | [
"image_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum() | code |
128045003/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre
top_instrumental_songs = ds[['instrum... | code |
128045003/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.c... | code |
128045003/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre | code |
128045003/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape
ds.columns
len(ds.columns)
top_ten_genre = ds.groupby('artists').count().sort_values(by='name', ascending=False)['name'][:10]
top_ten_genre
top_loudest_tracks = ds[['loudness', ... | code |
128045003/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
ds = pd.read_csv('/kaggle/input/spotify-datacsv/spotify_data.csv', dtype={'19': float})
ds
ds.isna().sum()
ds.shape | code |
50219835/cell_2 | [
"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 |
50219835/cell_8 | [
"text_plain_output_1.png"
] | from pytorch_tabnet.tab_model import TabNetClassifier
from pytorch_tabnet.tab_model import TabNetClassifier
clf = TabNetClassifier()
clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], max_epochs=2) | code |
50219835/cell_3 | [
"text_plain_output_1.png"
] | !pip install pytorch-tabnet | code |
18140562/cell_21 | [
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.svm import LinearSVC
import pandas a... | code |
18140562/cell_25 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.svm import LinearSVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
impor... | code |
18140562/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_excel('../input/Data_Train.xlsx')
df_test = pd.read_excel('../input/Data_Test.xlsx')
df_train.sample(5) | code |
18140562/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_excel('../input/Data_Train.xlsx')
df_test = pd.read_excel('../input/Data_Test.xlsx')
df_train.sample(5)
df_train.isna().sum() | code |
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