path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105195570/cell_21 | [
"image_output_1.png"
] | from scipy.stats import boxcox
from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Vic... | code |
105195570/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns | code |
105195570/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum() | code |
105195570/cell_19 | [
"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",
"image_output_10.png",
"image_output_9.png"
] | from scipy.stats import boxcox
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}... | code |
105195570/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.head() | code |
105195570/cell_18 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy.stats import boxcox
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}... | code |
105195570/cell_8 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape | code |
105195570/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.ren... | code |
105195570/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.ren... | code |
105195570/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.describe() | code |
105195570/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.info() | code |
129021961/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kaggle/input/az... | code |
129021961/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
final_y = to_categorical(y, num_classes=26)
fi... | code |
129021961/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
X.iloc[0].shape
imag... | code |
129021961/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape | code |
129021961/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kaggle/input/az... | code |
129021961/cell_11 | [
"text_html_output_1.png"
] | from tensorflow.keras.utils import to_categorical | code |
129021961/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-al... | code |
129021961/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 |
129021961/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
X.iloc[0].shape | code |
129021961/cell_3 | [
"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)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.head(5) | code |
129021961/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary() | code |
129021961/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import cv2
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kag... | code |
129021961/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
final_y = to_categorical(y, num_classes=26)
fi... | code |
105173227/cell_21 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
l1 = []
for i in range(1, 6):
l1.append(i ** 2)
l2... | code |
105173227/cell_9 | [
"text_plain_output_1.png"
] | l1 = []
for i in range(1, 6):
l1.append(i ** 2)
print(l1)
l2 = [i ** 2 for i in range(1, 6)]
print(l2) | code |
105173227/cell_4 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))... | code |
105173227/cell_23 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))... | code |
105173227/cell_6 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))... | code |
105173227/cell_2 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
print(p)
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
print(p2)
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
for i in age:
print('{:<10} - {}'.format(i, age[i]))
a = 'profit is {:,}'
b = ... | code |
105173227/cell_19 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
l1 = []
for i in range(1, 6):
l1.append(i ** 2)
l2... | code |
105173227/cell_15 | [
"text_plain_output_1.png"
] | d2 = {'anshu': 45, 'ayush': 42, 'moon': 12, 'bapun': 23}
d3 = {key: 'Yes' if value > 40 else 'No' for key, value in d2.items()}
print(d3) | code |
105173227/cell_17 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))... | code |
105173227/cell_14 | [
"text_plain_output_1.png"
] | d1 = {i: i ** 3 for i in range(1, 11)}
print(d1) | code |
105173227/cell_10 | [
"text_plain_output_1.png"
] | l3 = []
for i in range(1, 11):
if i % 2 == 0:
l3.append(i)
print(l3)
l4 = [i for i in range(1, 11) if i % 2 == 0]
print(l4) | code |
105173227/cell_12 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
s1 = {i for i in range(1, 11) if i % 2 == 0}
print(s1)... | code |
105173227/cell_5 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))... | code |
122256158/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Bidirectional
from tensorflow.keras.layers import Conv1D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import MaxPooling1D
import tensorflow as tf
class BiLSTM(tf.ke... | code |
122256158/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the... | code |
122256158/cell_11 | [
"text_html_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
122256158/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the... | code |
122256158/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the... | code |
122256158/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the... | code |
129020042/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | data = pd.read_csv('/kaggle/input/sd2gpt2/gpt_generated_prompts.csv') | code |
89132100/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lookupLayersMap = dict()
for column in categorical_features:
unique_values = list(train[column].unique())
lookupLayersMap[column] = tf.keras.layers.StringLookup(vocabulary=unique_values) | code |
89132100/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inpla... | code |
89132100/cell_11 | [
"text_html_output_1.png"
] | from tensorflow import keras
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None... | code |
89132100/cell_19 | [
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inpla... | code |
89132100/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown... | code |
89132100/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown... | code |
89132100/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inpla... | code |
89132100/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unkno... | code |
50234066/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import reverse_geocoder as rg
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.lati... | code |
50234066/cell_13 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.total_rooms.unique(), len(train_df.total_rooms.unique()))
train_df.total_rooms.plot(... | code |
50234066/cell_23 | [
"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
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df... | code |
50234066/cell_6 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.describe() | code |
50234066/cell_11 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.house_value.unique(), len(train_df.house_value.unique()))
train_df.house_value.plot(... | code |
50234066/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import reverse_geocoder as rg
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.lati... | code |
50234066/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g... | code |
50234066/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum() | code |
50234066/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.latitude))
train_df | code |
50234066/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)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df['total_bedrooms'].mean() | code |
50234066/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.population.unique(), len(train_df.population.unique()))
train_df.population.plot() | code |
50234066/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df.households.plot() | code |
50234066/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df.house_value.plot() | code |
50234066/cell_14 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.total_bedrooms.unique(), len(train_df.total_bedrooms.unique()))
train_df.total_bedro... | code |
50234066/cell_10 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum() | code |
50234066/cell_12 | [
"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/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.housing_median_age.unique(), len(train_df.housing_median_age.unique()))
train_df.hou... | code |
50234066/cell_5 | [
"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)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df | code |
105179122/cell_4 | [
"text_plain_output_1.png"
] | b = 2.3456
print(b) | code |
105179122/cell_6 | [
"text_plain_output_1.png"
] | c = 'world'
print(c) | code |
105179122/cell_2 | [
"text_plain_output_1.png"
] | a = 10
print(a) | code |
105179122/cell_7 | [
"text_plain_output_1.png"
] | c = 'world'
type(c) | code |
105179122/cell_3 | [
"text_plain_output_1.png"
] | a = 10
type(a) | code |
105179122/cell_5 | [
"text_plain_output_1.png"
] | b = 2.3456
type(b) | code |
34120028/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences =... | code |
34120028/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences =... | code |
34120028/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 |
34120028/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences =... | code |
34120028/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.num... | code |
34120028/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences =... | code |
73067347/cell_4 | [
"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)
df = pd.read_csv('train.csv')
df.info()
df.isnull().sum() | code |
73067347/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
cats = df.dtypes == 'object'
object_cols = list(cats[cats].index)
print('Categorical Columns')
print(object_cols) | code |
73067347/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.chdir('/kaggle/input/30-days-of-ml')
os.listdir() | code |
73067347/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
cats = df.dtypes == 'object'
object_cols = list(cats[cats].index)
cat_features = [cat_val for cat_val in df.columns if 'cat' in cat_val]
print(cat_features)
num_cols = [col for col in df.columns if... | code |
73067347/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df | code |
73067347/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
df.describe(include='all') | code |
18147692/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
sns.boxplot(data_age1.age) | code |
18147692/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape | code |
18147692/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset... | code |
18147692/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
import pandas_profiling as pp
pp.ProfileReport(dataset) | code |
18147692/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset... | code |
18147692/cell_26 | [
"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
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
d... | code |
18147692/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset... | code |
18147692/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
18147692/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
dataset['age'].value_counts().head() | code |
18147692/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
data1000... | code |
18147692/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset... | code |
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