path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129016727/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 |
129016727/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Page_Rank'].value_counts() | code |
129016727/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_sco... | code |
129016727/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('... | code |
129016727/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['double_slash_redirecting'].value_counts() | code |
129016727/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Prefix_Suffix'].value_counts() | code |
129016727/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)... | code |
129016727/cell_27 | [
"text_html_output_1.png"
] | print(X_train.shape)
print(X_test.shape) | code |
105200156/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item... | code |
105200156/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text)... | code |
105200156/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3 | code |
105200156/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text)... | code |
105200156/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021 | code |
105200156/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item... | code |
105200156/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
105200156/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').... | code |
90147420/cell_6 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trac... | code |
90147420/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df | code |
90147420/cell_11 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import math
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)... | code |
90147420/cell_7 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trac... | code |
90147420/cell_3 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(go.Scatter(x=df.Date, y=df.Close, name='Close'))
fig.add_trace(g... | code |
90147420/cell_12 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import math
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)... | code |
90147420/cell_5 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trac... | code |
2014493/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.... | code |
2014493/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info() | code |
2014493/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2014493/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2014493/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all... | code |
2014493/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((tra... | code |
2014493/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
34120753/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_9 | [
"image_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_25 | [
"text_plain_output_1.png"
] | from nltk import tokenize
import os
import pandas as pd
import string
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
o... | code |
34120753/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install --upgrade pandas-profiling
!pip install --upgrade hypertools
!pip install --upgrade pandas | code |
34120753/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_22 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_10 | [
"text_html_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
34120753/cell_27 | [
"text_plain_output_1.png"
] | from nltk import tokenize
import matplotlib.pyplot as plt
import os
import pandas as pd
import string
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'... | code |
34120753/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'subm... | code |
50228570/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.metrics import accuracy_score
import numpy as np
digits = load_digits()
X = digits.data
y = digits.target
p = 0.75
idx = int(p * X.shape[0]) + 1
X_train, X_test = np.split(X, [idx])
y_train, y_test = np.split(y, [idx])
def euclidian_metric(x, y):
return np.... | code |
50228570/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
digits = load_digits()
X = digits.data
y = digits.target
print(digits.DESCR) | code |
50228570/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier(n_estimators=1000)
rf_clf.fit(X_train, y_train) | code |
50228570/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
rf_clf = RandomForestClassifier(n_estimators=1000)
rf_clf.fit(X_train, y_train)
y_pred_rf = rf_clf.predict(X_test)
rf_err_rate = 1 - accuracy_score(y_test, y_pred_rf)
print('Random forest classifier error: ' + str(rf_err_r... | code |
32072376/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0] | code |
32072376/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_2 | [
"text_plain_output_1.png"
] | import os
import json
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 |
32072376/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns | code |
32072376/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | # return the top 10 categories by trending video frequency using .value_counts()
youtube['category_id']\
.value_counts()\ | code |
32072376/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views... | code |
32072376/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
youtube.tail(3) | code |
32072376/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)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.head() | code |
122247715/cell_42 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['embarked'].value_counts() | code |
122247715/cell_63 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.g... | code |
122247715/cell_9 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.info() | code |
122247715/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who']) | code |
122247715/cell_56 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
titanic['who'].value_counts() | code |
122247715/cell_34 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['who'].value_counts() | code |
122247715/cell_23 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embarked'].value_counts() | code |
122247715/cell_30 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum() | code |
122247715/cell_39 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['pclass'].value_counts() | code |
122247715/cell_26 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embark_town'].mode()[0] | code |
122247715/cell_65 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.g... | code |
122247715/cell_41 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['sibsp'].value_counts() | code |
122247715/cell_54 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum() | code |
122247715/cell_11 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum() | code |
122247715/cell_60 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.g... | code |
122247715/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['age'].median() | code |
122247715/cell_45 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['survived'].value_counts() | code |
122247715/cell_18 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['age'].mean() | code |
122247715/cell_32 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.head() | code |
122247715/cell_51 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
x = ['class', 'age', 'sibsp', 'parch', 'fare', 'embarked', 'who', 'alone']
y = ['... | code |
122247715/cell_59 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.g... | code |
122247715/cell_58 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.g... | code |
122247715/cell_28 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum() | code |
122247715/cell_8 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.head() | code |
122247715/cell_15 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0] | code |
122247715/cell_38 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['class'].value_counts() | code |
122247715/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
plt.hist(titanic['age']) | code |
122247715/cell_35 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['sex'].value_counts() | code |
122247715/cell_43 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['embark_town'].value_counts() | code |
122247715/cell_46 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['alive'].value_counts() | code |
122247715/cell_24 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embarked'].mode()[0] | code |
122247715/cell_14 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0] | code |
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