path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122264561/cell_99 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_55 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_76 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_92 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_91 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
df_profile2 = df_profile.explode('Interests')
df_profile2 | code |
122264561/cell_65 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_73 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_61 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_72 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_con... | code |
122264561/cell_69 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_86 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
df_profile | code |
122264561/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/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 |
122264561/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_82 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practic... | code |
122264561/cell_51 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_62 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_95 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_80 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_66 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_77 | [
"text_plain_output_1.png"
] | code | |
122264561/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_97 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_71 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practic... | code |
122264561/cell_70 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_85 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df.explode('my_list_column') | code |
325724/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
from sklearn.preprocessing import LabelEncoder
from scipy.sparse import csr_matrix, hstack
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics ... | code |
325724/cell_7 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.cs... | code |
325724/cell_8 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.cs... | code |
325724/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.cs... | code |
327693/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float... | code |
327693/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label']==column].iloc[row]... | code |
327693/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float... | code |
327693/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
print(train_data.shape)
print(test_data.shape) | code |
327693/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label'] == column].iloc[row].drop('label').as_matri... | code |
48165682/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
... | code |
48165682/cell_4 | [
"image_output_1.png"
] | !pip install plotly
!pip install cufflinks
!pip install textblob | code |
48165682/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 |
48165682/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_... | code |
48165682/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
... | code |
48165682/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
... | code |
48165682/cell_8 | [
"image_output_1.png"
] | import cufflinks as cf
import plotly as py
py.offline.init_notebook_mode(connected=True)
cf.go_offline() | code |
48165682/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../in... | code |
48165682/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
... | code |
48165682/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.head() | code |
48165682/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
print('Rows :', df.shape[0])
print('Columns :', df.shape[1])
print('\nFeatures :\n :', df.columns.tolist())
print('\nMissing values :', df.isnull().values.sum())
print('\nUn... | code |
72075672/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = p... | code |
72075672/cell_2 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/housingdataset/test.csv', index_col='Id')
X_full.dropna(axis=0... | code |
122254015/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | a = 2
b = 4
c = 5
d = a + b + c
type(d)
a = 2
b = 3
c = a
a = b
b = c
print(a, b)
type(b)
type(a) | code |
122254015/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | Sales_Store_A = input('Put your sale') | code |
122254015/cell_2 | [
"text_plain_output_1.png"
] | Revenue = 1200
CostofSales = 750
CostofMarketing = 100
NetProfitMargin = int((Revenue - (CostofSales + CostofMarketing)) / Revenue * 100)
print(NetProfitMargin, 'Percentage') | code |
122254015/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | a = 2
b = 4
c = 5
d = a + b + c
print(d)
type(d) | code |
104113435/cell_23 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['... | code |
104113435/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def ... | code |
104113435/cell_15 | [
"text_plain_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = ... | code |
104113435/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet(... | code |
104113435/cell_14 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = ... | code |
104113435/cell_22 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['line... | code |
129014767/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
pd.Series([1, 2, 3, 4, 5]) | code |
129014767/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series | code |
129014767/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series | code |
129014767/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series | code |
129014767/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_16 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1,... | code |
129014767/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series | code |
105181512/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
df = pd.read_csv('haberman.csv')
df.head() | code |
17105960/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_... | code |
17105960/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.isnull().values.any())
print(test.isnull().values.any()) | code |
17105960/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
print(train.dtypes.value_counts())
print(test.dtypes.value_counts()) | code |
17105960/cell_2 | [
"text_html_output_1.png"
] | import os
import pandas as pd
print(os.listdir('../input'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') | code |
17105960/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_... | code |
17105960/cell_7 | [
"text_html_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
train.describe() | code |
17105960/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_... | code |
17105960/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.o... | code |
17105960/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.d... | code |
17105960/cell_3 | [
"text_html_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
17105960/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.d... | code |
17105960/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_... | code |
17105960/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_... | code |
104126055/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, Y_train)
Y_pred = classifier.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred) * ... | code |
104126055/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Sex'].unique() | code |
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