path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122248046/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
df.head() | code |
122248046/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.head() | code |
122248046/cell_17 | [
"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)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
popular_cities = df['... | code |
16125131/cell_21 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
print(f"total foreign population: {df['IBGE_RES_POP_ESTR'].sum():10.0f}")
print(f"% of foreign population {df['IBGE_RES_POP_ESTR'].sum() / df['IBGE_... | code |
16125131/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
df['STATE'].value_counts().shape | code |
16125131/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.head() | code |
16125131/cell_19 | [
"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)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'... | code |
16125131/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16125131/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'... | code |
16125131/cell_16 | [
"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/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
print(f"total population (2018): {df['ESTIMATED_POP'].sum():10.0f}")
avg_growth = (df['ESTIMATED_POP'].sum() - df['IBGE_RES_POP'].sum()) / df['IBGE_... | code |
16125131/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'... | code |
16125131/cell_14 | [
"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)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'... | code |
16125131/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
df['STATE'].value_counts() | code |
16125131/cell_12 | [
"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)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
plt.figure(figsize=(10, 10))
plt.title('C... | code |
16125131/cell_5 | [
"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/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.info() | code |
121151296/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)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.boxplot('x', da... | code |
121151296/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count() | code |
121151296/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample | code |
121151296/cell_23 | [
"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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.bar('x', 'y', d... | code |
121151296/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.cs... | code |
121151296/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
121151296/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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.plot('x', 'y', ... | code |
121151296/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.cs... | code |
121151296/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape | code |
121151296/cell_41 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/... | code |
121151296/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.info() | code |
121151296/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 |
121151296/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
test.head() | code |
121151296/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
121151296/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['y'].describe() | code |
121151296/cell_28 | [
"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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
... | code |
121151296/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.tail() | code |
121151296/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].value_counts() | code |
121151296/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['y'].value_counts() | code |
121151296/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.cs... | code |
121151296/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121151296/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].describe() | code |
121151296/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.cs... | code |
121151296/cell_43 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
121151296/cell_24 | [
"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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.boxplot('x', da... | code |
121151296/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].isnull().count() | code |
121151296/cell_22 | [
"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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.scatter('x', 'y... | code |
121151296/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape | code |
121151296/cell_27 | [
"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 pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropn... | code |
121151296/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.head() | code |
121151296/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.cs... | code |
322308/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import xgboost as xgb
from scipy import sparse
from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, scale
from sklearn.decomposition import T... | code |
322308/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
print('# Read App Events')
app_ev = pd.read_csv('../input/app_events.csv', dtype={'device_id': np.str})
app_ev = app_ev.groupby('event_id')['app_id'].apply(lambda x: ' '.join(set(('app_id:' + str(s) for s in x)))) | code |
322308/cell_5 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
app_ev = pd.read_csv('../input/app_events.csv', dtype={'device_id': np.str})
app_ev = app_ev.groupby('event_id')['app_id'].apply(lambda x: ' '.join(set(('app_id:' + str(s) for s in x))))
print('# Read Events')
events = pd.read_csv('../input/events.csv', dtype={'device_id': np.s... | code |
130010335/cell_9 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp... | code |
130010335/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
130010335/cell_23 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-... | code |
130010335/cell_20 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-... | code |
130010335/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
130010335/cell_11 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv... | code |
130010335/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn import model_selection
import warnings, gc
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130010335/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
130010335/cell_8 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.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
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progr... | code |
130010335/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_da... | code |
130010335/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
130010335/cell_17 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-... | code |
130010335/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
130010335/cell_22 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-... | code |
130010335/cell_10 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.re... | code |
130010335/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-predictio... | code |
130010335/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] =... | code |
32063980/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['customer_type'].unique() | code |
32063980/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data.info() | code |
32063980/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
from sklearn ... | code |
32063980/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.head() | code |
32063980/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as... | code |
32063980/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['reservation_status'].unique() | code |
32063980/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pan... | code |
32063980/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['arrival_date_month'] = data['arrival_date_... | code |
32063980/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.info() | code |
32063980/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['arrival_date_month'].unique() | code |
32063980/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 |
32063980/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/... | code |
32063980/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
print('Nan in each columns', data.isna().sum(), sep='\n') | code |
32063980/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['hotel'].unique() | code |
32063980/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['hotel'] = data['hotel'].map({'Resort Hotel... | code |
32063980/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import n... | code |
32063980/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as p... | code |
32063980/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['assigned_room_type'].unique() | code |
32063980/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['deposit_type'].unique() | code |
32063980/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.describe() | code |
32063980/cell_36 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_sel... | code |
90151799/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
dataset.head() | code |
90151799/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ense... | code |
90151799/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
print(objList) | code |
90151799/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
data... | code |
90151799/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestCl... | code |
90151799/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
data... | code |
90151799/cell_22 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_tr... | code |
90151799/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
data... | code |
18139612/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.head() | code |
18139612/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_1 | [
"text_plain_output_1.png"
] | !pip3 list |grep pandas
from pandas_datareader import data, wb
import numpy as np
import pandas as pd
import datetime
start = datetime.datetime(2006,1,1)
end = datetime.datetime(2016,1,1)
BAC = data.DataReader('BAC',"yahoo",start,end,)
C = data.DataReader('c',"yahoo",start,end)
GS = data.DataReader('GS',"yahoo",start,... | code |
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