path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105183805/cell_8 | [
"text_plain_output_1.png"
] | from efficientnet_pytorch import EfficientNet
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
print(IMSIZE) | code |
105183805/cell_15 | [
"text_plain_output_1.png"
] | from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7') | code |
105183805/cell_17 | [
"text_html_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from transformers import get_cosine_schedule_with_warmup
impor... | code |
105183805/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.cla... | code |
121154019/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_25 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categor... | code |
121154019/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
t... | code |
121154019/cell_20 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.... | code |
121154019/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.... | code |
121154019/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = ... | code |
121154019/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
t... | code |
121154019/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_te... | code |
121154019/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = ... | code |
121154019/cell_35 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
t... | code |
121154019/cell_24 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_test, cat_cols):
encoder = OneHotEncoder(categories='auto', sparse=False, handle_unknown='ignore')
encoder.fit(... | code |
121154019/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import xgboost as xgb
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def encode(X_train, X_te... | code |
121154019/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
def wrangle(df):
df['Male'] = df['Sex'].map(lambda x: x == 'male')
df['Pronoun'] = df['Name'].map(lambda x: x.split(', ')[1].split('.')[0])
df['Prefix'] = df['Ticket'].map(lambda... | code |
121154019/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
2045099/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.speaker_occupation.value_counts().head(10) | code |
2045099/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.describe() | code |
2045099/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig = plt.figure()
axes = fig.add_axes([0, 0, 1, 1])
axes.... | code |
2045099/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xla... | code |
2045099/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xla... | code |
2045099/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.head() | code |
2045099/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.event.value_counts().tail(10) | code |
2045099/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2045099/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted[ted['speaker_occupation'].isnull()] | code |
2045099/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
ted.event.value_counts().head(10) | code |
2045099/cell_32 | [
"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)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xla... | code |
2045099/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5) | code |
2045099/cell_38 | [
"text_plain_output_1.png"
] | import ast
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
... | code |
2045099/cell_31 | [
"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)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xla... | code |
2045099/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5) | code |
2045099/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr()
ted.sort_values('views', ascending=False).head(5)
ted.sort_values('views', ascending=True).head(5)
fig=plt.figure()
axes=fig.add_axes([0,0,1,1])
axes.set_xla... | code |
2045099/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.corr() | code |
2045099/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ted = pd.read_csv('../input/ted_main.csv')
ted.info() | code |
17115909/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies... | code |
17115909/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17115909/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
17115909/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)
imdb_movies = pd.read_csv('../input/imdb-movies.csv')
movie_count = imdb_movies.shape[0]
columns_to_keep = ['popularity', 'budget', 'revenue', 'original_title', 'director', 'runtime', 'genres', 'production_companies', 'release_year']
imdb_movies = ... | code |
1004380/cell_2 | [
"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('../input/train.csv')
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts... | code |
1004380/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004380/cell_3 | [
"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('../input/train.csv')
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts... | code |
105214007/cell_13 | [
"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
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 1... | code |
105214007/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.head() | code |
105214007/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/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 |
105214007/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_8 | [
"text_html_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
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum() | code |
105214007/cell_10 | [
"text_html_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
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
105214007/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.isna().sum()
import seaborn as sns
import matplotlib.pyplot as plt
data.gender.unique()
dat... | code |
122252043/cell_42 | [
"text_plain_output_1.png"
] | x = 12
y = 8
y >> 2 | code |
122252043/cell_63 | [
"text_plain_output_1.png"
] | int('0o65416', base=8) | code |
122252043/cell_81 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f"a={{{a:5d}}},b='{b:>4.0f}'") | code |
122252043/cell_13 | [
"text_plain_output_1.png"
] | 375 | code |
122252043/cell_9 | [
"text_plain_output_1.png"
] | 74155 | code |
122252043/cell_4 | [
"text_plain_output_1.png"
] | type('2+3') | code |
122252043/cell_83 | [
"text_plain_output_1.png"
] | num = input('A number:') | code |
122252043/cell_79 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>+7,d},b={b:>06.1f}') | code |
122252043/cell_20 | [
"text_plain_output_1.png"
] | num = 12
num = 12 + 7.2
type(num) | code |
122252043/cell_55 | [
"text_plain_output_1.png"
] | eval("int('2020')") | code |
122252043/cell_6 | [
"text_plain_output_1.png"
] | type(18) | code |
122252043/cell_74 | [
"text_plain_output_1.png"
] | print("'''\\n represents a new line character'''") | code |
122252043/cell_40 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x << 2 | code |
122252043/cell_29 | [
"text_plain_output_1.png"
] | 2 < 8 or (7 <= 8 and 7 > 2) | code |
122252043/cell_39 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x = 12
y = 8
x >> 2 | code |
122252043/cell_26 | [
"text_plain_output_1.png"
] | 'kitty' < 'kitten' | code |
122252043/cell_48 | [
"text_plain_output_1.png"
] | (not 'piggy') + True | code |
122252043/cell_73 | [
"text_plain_output_1.png"
] | print('A back slash \\ sign.') | code |
122252043/cell_41 | [
"text_plain_output_1.png"
] | x = 12
y = 8
~y | code |
122252043/cell_54 | [
"text_plain_output_1.png"
] | eval('6**2+3*(7-1)') | code |
122252043/cell_72 | [
"text_plain_output_1.png"
] | print('"You may say I\'m a dreamer"') | code |
122252043/cell_67 | [
"text_plain_output_1.png"
] | oct(int('0b1001001', base=2)) | code |
122252043/cell_11 | [
"text_plain_output_1.png"
] | 3737 | code |
122252043/cell_60 | [
"text_plain_output_1.png"
] | hex(1024) | code |
122252043/cell_86 | [
"text_plain_output_1.png"
] | nofd = input('A num of d:') | code |
122252043/cell_64 | [
"text_plain_output_1.png"
] | hex(int('0o65416', base=8)) | code |
122252043/cell_32 | [
"text_plain_output_1.png"
] | x = 5
x += 2
x | code |
122252043/cell_68 | [
"text_plain_output_1.png"
] | hex(int('0b1001001', base=2)) | code |
122252043/cell_62 | [
"text_plain_output_1.png"
] | bin(int('0o65416', base=8)) | code |
122252043/cell_59 | [
"text_plain_output_1.png"
] | oct(1024) | code |
122252043/cell_58 | [
"text_plain_output_1.png"
] | bin(1024) | code |
122252043/cell_28 | [
"text_plain_output_1.png"
] | False and 'kitty' or True | code |
122252043/cell_78 | [
"text_plain_output_1.png"
] | a = 12670
b = 12.344
print(f'a={a:>+7d},b={b:>06.2f}') | code |
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