path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18141020/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/... | code |
18141020/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd
from sk... | code |
18141020/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd
from sk... | code |
18141020/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd
from sk... | code |
18141020/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/... | code |
33122764/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
import requests
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sor... | code |
33122764/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import plotly.graph_objects as go
import re
import requests
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortn... | code |
33122764/cell_23 | [
"text_html_output_1.png"
] | import requests
import requests
import re
link2 = 'https://api.covid19india.org/data.json'
r = requests.get(link2)
india_Data = r.json()
link3 = 'https://api.covid19india.org/v2/state_district_wise.json'
r = requests.get(link3)
states_Data = r.json()
for i in range(len(states_Data[:])):
print(states_Data[i]['stat... | code |
33122764/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chills', 'Nausea or vomiting', 'Nasal congestion', 'Diarrhoea', 'Haemoptysis', 'Conjunctival congestion'], 'percentage': [87.9... | code |
33122764/cell_2 | [
"text_html_output_1.png"
] | import IPython
import IPython
IPython.display.HTML('<div class="flourish-embed flourish-bar-chart-race" data-src="visualisation/1977187" data-url="https://flo.uri.sh/visualisation/1977187/embed"><script src="https://public.flourish.studio/resources/embed.js"></script></div>') | code |
33122764/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chil... | code |
33122764/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chills', 'Nausea or vomiting', 'N... | code |
33122764/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chills', 'Nausea or vomiting', 'Nasal congestion', 'Diarrhoea'... | code |
33122764/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chills', 'Nausea or vomiting', 'Nasal congestion', 'Diarrhoea', 'Haemoptysis', 'Conjunctiva... | code |
33122764/cell_12 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
symptoms = {'symptom': ['Fever', 'Dry cough', 'Fatigue', 'Sputum production', 'Shortness of breath', 'Muscle pain', 'Sore throat', 'Headache', 'Chills', 'Nausea or vomiting', 'Nasal congestion', 'Diarrhoea', 'Haemoptysis', 'Conjunctiva... | code |
105179005/cell_13 | [
"text_plain_output_1.png"
] | a = "data anlytic's"
a.find('data')
a.split()
a = 'd24%343cbdcjh'
a.isalnum()
a = 'i am learning python'
l = len(a)
r = ' '
while l > 0:
r = r + a[l - 1]
l = l - 1
print(r) | code |
105179005/cell_9 | [
"text_plain_output_1.png"
] | b = ' we_are_doing_a_good_progress '
c = b.split('_')
b = b.strip()
c = b.lstrip()
b
b = 'dfffdsf'
b.isalpha() | code |
105179005/cell_4 | [
"text_plain_output_1.png"
] | a = "data anlytic's"
print(a[-3])
print(a[-8:-1])
print(a[0:9:2])
a.find('data')
a.split() | code |
105179005/cell_6 | [
"text_plain_output_1.png"
] | b = ' we_are_doing_a_good_progress '
c = b.split('_')
print(c[5])
b = b.strip()
c = b.lstrip()
b | code |
105179005/cell_2 | [
"text_plain_output_1.png"
] | a = "data anlytic's"
print(a) | code |
105179005/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | b = ' we_are_doing_a_good_progress '
c = b.split('_')
b = b.strip()
c = b.lstrip()
b
c = 'adnan k'
c.islower()
c = c.upper()
c
c = c.capitalize()
c | code |
105179005/cell_8 | [
"text_plain_output_1.png"
] | a = "data anlytic's"
a.find('data')
a.split()
a = 'd24%343cbdcjh'
a.isalnum() | code |
105179005/cell_10 | [
"text_plain_output_1.png"
] | b = ' we_are_doing_a_good_progress '
c = b.split('_')
b = b.strip()
c = b.lstrip()
b
c = 'adnan k'
c.islower()
c = c.upper()
c | code |
105179005/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | b = ' we_are_doing_a_good_progress '
c = b.split('_')
b = b.strip()
c = b.lstrip()
b
c = 'adnan k'
c.islower()
c = c.upper()
c
c = c.capitalize()
c
c = c.title()
c | code |
88081842/cell_21 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_9 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_20 | [
"text_html_output_1.png"
] | conversionVal = {'₹': 0.013, '$': 1, '£': 1.36, 'AFN': 0.011}
conversionVal | code |
88081842/cell_6 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88081842/cell_7 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_18 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_8 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
print('Dataset Shape: ', df.shape, '\n--------------------------------')
df.head() | code |
88081842/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_24 | [
"text_plain_output_1.png"
] | multiplier_per_year = {'yr': 1, 'mo': 12, 'hr': 2064}
multiplier_per_year | code |
88081842/cell_22 | [
"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/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns
xdf = df.rename(columns={'Company Name': 'company_name', 'Job Title': 'job_title', 'Salaries Reported': 'sal_reported', 'Location': 'location',... | code |
88081842/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('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.columns | code |
18146356/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.describe() | code |
18146356/cell_34 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
f,ax=plt.subplots(1,2,figsi... | code |
18146356/cell_30 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count()
pd.crosstab(df_train.Pcl... | code |
18146356/cell_20 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count() | code |
18146356/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
f,ax=plt.subplots(1,2,figsi... | code |
18146356/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count()
df_train[['Pclass', 'Sur... | code |
18146356/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.describe() | code |
18146356/cell_32 | [
"image_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count()
pd.crosstab(df_train.Pcl... | code |
18146356/cell_28 | [
"image_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count()
df_train[['Pclass', 'Sur... | code |
18146356/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.info() | code |
18146356/cell_15 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
msno.bar(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.2, 0.5, 0.2)) | code |
18146356/cell_38 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
df_train.groupby(['Sex', 'Survived'])['Survived'].count()
pd.crosstab(df_train.Pcl... | code |
18146356/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
f, ax = plt.subplots(1, 2, ... | code |
18146356/cell_14 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2)) | code |
18146356/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum()
msno.matrix(df=df_train.iloc[:, :], figsize=(7, 5), color=(0.5, 0.1, 0.2))
f,ax=plt.subplots(1,2,figsi... | code |
18146356/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.info() | code |
18146356/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.isnull().sum() | code |
18146356/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
50210546/cell_21 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1 | code |
50210546/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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 statsmodels.api as sm
import statsmode... | code |
50210546/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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 statsmodels.api as sm
import statsmode... | code |
50210546/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas
import os
import gc
import pylab
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr, probplot, norm, shapiro
import statsmodels.api as sm
pal = sns.color_palette()
pd.set_option('display.max_columns', 50)
import plotly.offline as p... | code |
50210546/cell_1 | [
"text_plain_output_1.png"
] | !pip install bioinfokit | code |
50210546/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_18 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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 statsmodels.api as sm
import statsmode... | code |
50210546/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df[df['position'] != 'top'][df['position'] != 'bottom'][df['position'] != 'middle'][df['position'] != 'leaderboard'][df['position'] != 'passback'] | code |
50210546/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df1 = pd.read_csv('/kaggle/input/dash-assignment/Actual_eCPM.csv')
df1 | code |
50210546/cell_16 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_27 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df['position'] = np.nan
list = ['top']
val = df['AD_UNIT_NAME'].apply(lambda x: any([k in x for k in list]))
my_ind = [i for i... | code |
50210546/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_excel('/kaggle/input/dash-assignment/DFP.xlsx', 'Data')
df1
df = df1
df.head(3) | code |
73096186/cell_13 | [
"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 = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2)
df.corr() | code |
73096186/cell_30 | [
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_sc... | code |
73096186/cell_33 | [
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.impute import SimpleImputer
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, RandomizedSearchCV
from sklearn.pi... | code |
73096186/cell_44 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_sc... | code |
73096186/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import shap
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import eli5
from eli5.sklearn import PermutationImportance
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
from xgboost import XGBRegressor
from sklearn.impute import SimpleImputer
from sklearn.... | code |
73096186/cell_29 | [
"text_html_output_2.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_score
y_pred = [y_train.mean()] * len(y_train)
mean_b... | code |
73096186/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
df.info() | code |
73096186/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 |
73096186/cell_45 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_sc... | code |
73096186/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
df.head(5) | code |
73096186/cell_15 | [
"text_html_output_1.png"
] | 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 = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2)
df.corr()
correlations = df... | code |
73096186/cell_16 | [
"text_plain_output_1.png"
] | 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 = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2)
df.corr()
correlations = df... | code |
73096186/cell_17 | [
"image_output_1.png"
] | 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 = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2)
df.corr()
correlations = df... | code |
73096186/cell_46 | [
"image_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from eli5.sklearn import PermutationImportance
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.metrics import roc_curve, plot_roc_curve, mean... | code |
73096186/cell_14 | [
"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 = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2)
df.corr()
correlations = df.corr(method='pearson')
print(correlations['price actual... | code |
73096186/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
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 plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_da... | code |
73096186/cell_27 | [
"image_output_1.png"
] | from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_score
y_pred = [y_train.mean()] * len(y_train)
mean_baseline_pred = y_train.mean()
baseline_mae = mean_absolute_error(y_train, y_pred)
baseline_rmse = mean_squared_error(y_train, y_pred, squared=False)
print('Mean ... | code |
73096186/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/energy-consumption-generation-prices-and-weather/energy_dataset.csv')
round(df.isnull().sum() / len(df) * 100, 2) | code |
73096186/cell_36 | [
"text_plain_output_1.png"
] | from category_encoders import OneHotEncoder, OrdinalEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import roc_curve, plot_roc_curve, mean_absolute_error, mean_squared_error, accuracy_score
from sklearn.model_selection import GridSearchCV, Ran... | code |
74070897/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 |
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