path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50216735/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test.head() | code |
50216735/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
MNB = MultinomialNB()
MNB.fit(X_train, Y_train) | code |
50216735/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test.head() | code |
88097739/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)
data = pd.read_csv('../input/winter-olympic-medals-1924-2018/Winter_Olympic_Medals.csv')
data.dtypes
data_type = pd.DataFrame(data.dtypes).T.rename({0: 'Column Data Type:'})
data_type
data.describe() | code |
88097739/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winter-olympic-medals-1924-2018/Winter_Olympic_Medals.csv')
data.dtypes
data_type = pd.DataFrame(data.dtypes).T.rename({0: 'Column Data Type:'})
data_type | code |
88097739/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 |
88097739/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)
data = pd.read_csv('../input/winter-olympic-medals-1924-2018/Winter_Olympic_Medals.csv')
data.head() | code |
88097739/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winter-olympic-medals-1924-2018/Winter_Olympic_Medals.csv')
data.dtypes | code |
88097739/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)
data = pd.read_csv('../input/winter-olympic-medals-1924-2018/Winter_Olympic_Medals.csv')
data.dtypes
data_type = pd.DataFrame(data.dtypes).T.rename({0: 'Column Data Type:'})
data_type
data.info() | code |
17144256/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == ... | code |
17144256/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lam... | code |
17144256/cell_4 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lam... | code |
17144256/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lambda x: str(x))
df_test['Embarked'] = df_test.Emba... | code |
17144256/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
print(os.listdir('../input')) | code |
17144256/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lam... | code |
17144256/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lambda x: str(x))
df_test['Embarked'] = df_test.Emba... | code |
17144256/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == 'male' else 0)
df['Embarked'] = df.Embarked.apply(lam... | code |
17144256/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df['Sex'] = df.Sex.apply(lambda x: 1 if x == 'male' else 0)
df_test['Sex'] = df_test.Sex.apply(lambda x: 1 if x == ... | code |
105205396/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_23 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_6 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_7 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_15 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_16 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_14 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
105205396/cell_22 | [
"text_html_output_1.png"
] | import os
import pandas as pd
pathvals = ['/kaggle/input/internet-prices-datasets-for-analysis', './data']
fnames = ['average_after_tax_wages.csv', 'average_monthly_internet.csv', 'GDP_per_capita.csv', 'internet_adoption.csv']
average_monthly_internet = 'average_monthly_internet'
average_monthly_internet_costs = 'ave... | code |
32071401/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
32071401/cell_4 | [
"image_output_1.png"
] | import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
Iris.head(n=10) | code |
32071401/cell_20 | [
"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
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
32071401/cell_2 | [
"image_output_1.png"
] | import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.head(n=10) | code |
32071401/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32071401/cell_7 | [
"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
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
fig = Iris[Iris.Species == 'Iris-setosa'].plot(kind='scatter', x='SepalLengthCm', y='S... | code |
32071401/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)
import seaborn as sns
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
32071401/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
32071401/cell_3 | [
"image_output_1.png"
] | import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.info()
Iris.isnull().sum() | code |
32071401/cell_22 | [
"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
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
32071401/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # Data Processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
Iris = pd.read_csv('../input/iris/Iris.csv')
Iris.isnull().sum()
Iris.drop('Id', axis=1, inplace=True)
#Exploratory Data Analysis
#Sepal Length VS Sepal Width
fig=Iris[Iris.Species=='Iris-s... | code |
104124854/cell_4 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transported', 1)
y = df['Transported'].apply(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=... | code |
104124854/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transp... | code |
104124854/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transported', 1)
y = df['Transported'].apply(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=... | code |
104124854/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 |
104124854/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transp... | code |
104124854/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
impo... | code |
104124854/cell_3 | [
"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/spaceship-titanic/train.csv')
df.head() | code |
104124854/cell_17 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transp... | code |
104124854/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pandas as pd # data processing, CSV file ... | code |
104124854/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
X = df.drop('Transp... | code |
32071416/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
32071416/cell_4 | [
"text_html_output_1.png"
] | !curl -o $confirmed_csv $confirmed_gitpath | code |
32071416/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
32071416/cell_2 | [
"text_html_output_1.png"
] | !pip install folium | code |
32071416/cell_11 | [
"text_plain_output_1.png"
] | import math
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
i... | code |
32071416/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 |
32071416/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
32071416/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)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
32071416/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
32071416/cell_12 | [
"text_html_output_1.png"
] | from folium.plugins import TimestampedGeoJson
import folium
import math
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_cov... | code |
32071416/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)
confirmed_csv = 'corona_confirmed.csv'
confirmed_gitpath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
import pandas ... | code |
16162621/cell_42 | [
"image_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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_63 | [
"image_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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_21 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
df['Departments'].value_counts() | code |
16162621/cell_57 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_34 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
16162621/cell_44 | [
"text_html_output_1.png"
] | from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
import plotly.plotly as py
import plotly.graph_objs as go
init_notebook_mode(connecte... | code |
16162621/cell_20 | [
"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 seaborn as sns
df = pd.read_csv('../input/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
sns.countplot(x='Reason... | code |
16162621/cell_55 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_6 | [
"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/911.csv')
df.head() | code |
16162621/cell_40 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
16162621/cell_29 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
df.head() | code |
16162621/cell_26 | [
"image_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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
type(df['timeStamp'].iloc[0]) | code |
16162621/cell_65 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
16162621/cell_48 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_61 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
16162621/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)
df = pd.read_csv('../input/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
df['Reason'].value_counts().head(1) | code |
16162621/cell_50 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_52 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16162621/cell_45 | [
"image_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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
... | code |
16162621/cell_32 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
df.head() | code |
16162621/cell_59 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
16162621/cell_8 | [
"image_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/911.csv')
df['zip'].value_counts().head(5) | code |
16162621/cell_15 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df.head() | code |
16162621/cell_38 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
Month_grouping = df.groupby('Month').count()
M... | code |
16162621/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)
df = pd.read_csv('../input/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
df.head() | code |
16162621/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)
df = pd.read_csv('../input/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(':')[1])
type(df['timeStamp'].iloc[0]) | code |
16162621/cell_10 | [
"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/911.csv')
df['twp'].value_counts().head(5) | code |
16162621/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/911.csv')
df['title'].nunique() | code |
16162621/cell_36 | [
"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/911.csv')
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Departments'] = df['title'].apply(lambda title: title.split(... | code |
325103/cell_4 | [
"text_plain_output_1.png"
] | from sklearn import linear_model, svm, metrics
from sklearn import linear_model, svm, metrics
classifier = linear_model.SGDClassifier(n_iter=100, n_jobs=6, penalty='l1')
print(classifier) | code |
325103/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
325103/cell_5 | [
"text_plain_output_1.png"
] | from sklearn import linear_model, svm, metrics
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
target = dataset[[0]].values.ravel()
train = dataset.iloc[:, 1:].values
test = pd.read_csv('../input/test.csv').values
from sklearn import linear_model, sv... | code |
90118084/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=columns)
for col in column... | code |
90118084/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=columns)
for col in column... | code |
90118084/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=columns)
for col in column... | code |
90118084/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
data.head() | code |
90118084/cell_29 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
def visualize_word_counts(counts):
wc = WordCloud(max_font_size=130, min_font_size=25, colormap='tab20', background_color='white', prefer_horizontal=0.95, width=2100, height=700, random_state=0)
cloud = wc.generate_from_frequ... | code |
90118084/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=columns)
for col in column... | code |
90118084/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=column... | code |
90118084/cell_24 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def visualize_word_counts(counts):
wc = WordCloud(max_font_size=130, min_font_size=25, colormap='tab20', background_color='white', prefer_horizontal=0.95, width=2100, height=700, random_state=0)
cloud = wc... | code |
90118084/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/suicide-rates-worldwide-20002019/data.csv')
columns = ['Country', 'Year', 'ProbDyingBoth', 'ProbDyingMale', 'ProbDyingFemale', 'SuicideBoth', 'SuicideMale', 'SuicideFemale']
values = data.iloc[1:, :].values
data = pd.DataFrame(values, columns=columns)
for col in column... | code |
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