path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17120135/cell_9 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df_main = pd.read_csv('../input/zomato.csv')
df_main.head(1) | code |
17120135/cell_33 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,1... | code |
17120135/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,1... | code |
17120135/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_main = pd.read_csv('../input/zomato.csv')
df_main.info() | code |
17120135/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM = df_main.loc[df_main['location'] == 'BTM']
df_BTM_REST = df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20, 10))
ax1 = fig.ad... | code |
17120135/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_su... | code |
17120135/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,1... | code |
17120135/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,1... | code |
17120135/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_main = pd.read_csv('../input/zomato.csv')
df_main.describe() | code |
33107127/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
from sklearn.metrics import classification_report
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)
pred2 = model.predict(X_test)
print(classification_report(y_test, pred2)) | code |
33107127/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
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33107127/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred1 = lr.predict(X_test)
print(classification_report(y_test, pred1)) | code |
33107127/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/league-of-legends-diamond-ranked-games-10-min/high_diamond_ranked_10min.csv')
data.head() | code |
33107127/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
from sklearn.neighbors import KNeighborsClassifier
knnscore = []
for i, k in enumerate(range(1, 40)):
knn = KNeighborsClass... | code |
33107127/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
pred4 = dt.predict(X_test)
print(classification_report(y_test, pred4)) | code |
33107127/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
from sklearn.neighbors import KNeighborsClassifier
knnscore = []
for i, k in enumerate(range(1, 40)):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
... | code |
2008917/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotlib.ticker import M... | code |
2008917/cell_4 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/WorldPopulation.csv', encoding='ISO-8859-1')
data.head(10) | code |
2008917/cell_2 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import plotly.offline as py
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
plt.style.use('fivethirtyeigh... | code |
2008917/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.offline as py
import seaborn as sns
import squarify
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotli... | code |
2008917/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.offline as py
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotlib.ticker import MultipleLocator, FormatS... | code |
2008917/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotlib.ticker import M... | code |
2008917/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.offline as py
import seaborn as sns
import squarify
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import squarify
import matplotlib.ticker as plticker
from matplotli... | code |
2008917/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/WorldPopulation.csv', encoding='ISO-8859-1')
index_min = np.argmin(data['2016'])
index_max = np.argmax(data['2016'])
unit_min = data['Country'].values[index_min]
unit_max = data['Country'].values[index_max]
print('The most populated political unit:'... | code |
72084932/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.co... | code |
72084932/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
34134329/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes | code |
34134329/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes
cleaned_data.dtypes
... | code |
34134329/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34134329/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes
cleaned_data.dtypes
... | code |
34134329/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes
cleaned_data.dtypes
... | code |
34134329/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum() | code |
34134329/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.head(2) | code |
34134329/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes | code |
34134329/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.head(2) | code |
34134329/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df['Neighbourhood'].unique() | code |
34134329/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes
cleaned_data.dtypes
... | code |
34134329/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum() | code |
34134329/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.dtypes
cleaned_data.dtypes | code |
1004118/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1004118/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any() | code |
1004118/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
sns.heatmap(df.corr(), vmax=0.8, square=True, annot=True, fmt='.2f') | code |
1004118/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1004118/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1004118/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fi... | code |
1004118/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import ma... | code |
1004118/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import ma... | code |
1004118/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.describe() | code |
1004118/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1004118/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import ma... | code |
1004118/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as ... | code |
1004118/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
130016562/cell_9 | [
"text_plain_output_1.png"
] | patient_check_dict = {}
use_model_ratio = 0
first_cb_huber_use_ratio = {'updrs_1': 0.8, 'updrs_2': 0.8, 'updrs_3': 0.3, 'updrs_4': 0}
first_cb_mae_use_ratio = {'updrs_1': 0.2, 'updrs_2': 0.8, 'updrs_3': 0.1, 'updrs_4': 0}
cb_huber_use_ratio = {'updrs_1': 0.4, 'updrs_2': 0.5, 'updrs_3': 0.6, 'updrs_4': 0.5}
cb_mae_use_r... | code |
130016562/cell_4 | [
"text_plain_output_5.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"t... | import pandas as pd
first_linear_trend_df = pd.read_csv('/kaggle/input/amp-visitmonth-model-first-month/first_linear_trend_df.csv')
first_cb_trend_huber_df = pd.read_csv('/kaggle/input/amp-visitmonth-model-first-month/first_cb_trend_huber_df.csv')
first_cb_trend_mae_df = pd.read_csv('/kaggle/input/amp-visitmonth-model... | code |
90130234/cell_9 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_id')
train.time = pd.to_datetime(... | code |
90130234/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train.head() | code |
90130234/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 |
90130234/cell_7 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_id')
train.time = pd.to_datetime(... | code |
90130234/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_id')
train.time = pd.to_datetime(... | code |
90130234/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)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_i... | code |
90130234/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)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_i... | code |
90130234/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)
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
train = train.set_index('row_id')
test = test.set_index('row_i... | code |
122249704/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
import pandas as pd | code |
88101809/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import os
import collections
import csv
import matplotlib.pyplot as plt
import re
import csv
import matplotlib.pyplot as plt
import statistics as st
import re
import collections
from glob import os
import pandas as pd
arr = os.listdir('../input/spectra-files/')
arr = sorted(arr, key=lambda x: int(os.path... | code |
88101809/cell_11 | [
"text_plain_output_1.png"
] | from glob import os
import collections
import csv
import matplotlib.pyplot as plt
import re
import csv
import matplotlib.pyplot as plt
import statistics as st
import re
import collections
from glob import os
import pandas as pd
arr = os.listdir('../input/spectra-files/')
arr = sorted(arr, key=lambda x: int(os.path... | code |
88101809/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import os
import csv
import matplotlib.pyplot as plt
import statistics as st
import re
import collections
from glob import os
import pandas as pd
arr = os.listdir('../input/spectra-files/')
arr = sorted(arr, key=lambda x: int(os.path.splitext(x)[0]))
print(arr) | code |
88101809/cell_5 | [
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt
def function_general(file_name):
wav = []
absr = []
d = {}
with open(file_name, 'r') as df:
reader = csv.reader(df)
header = next(reader)
print(header)
for row in reader:
d[float(row[0])] = float(row[1])
... | code |
106192098/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')... | code |
106192098/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
ss = pd.read_csv('../input/tabular-playground-series... | code |
106192098/cell_1 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
display(train)
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
display(test)
ss = pd.read_csv('../in... | code |
106192098/cell_7 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-s... | code |
106192098/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
ss = pd.read_csv('../input/tabul... | code |
106192098/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')... | code |
32068555/cell_48 | [
"text_plain_output_1.png"
] | !pip install json2html
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
from json2html import *
from IPython.core.display import display, HTML | code |
32068555/cell_50 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"image_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"image_output_7.png",
"text_html_... | from IPython.core.display import display, HTML
from nltk.tokenize import sent_tokenize
from nltk.tokenize import sent_tokenize
from wordcloud import WordCloud, STOPWORDS
import json
import json
import json
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import os
imp... | code |
32068555/cell_36 | [
"text_plain_output_1.png"
] | !python -m pip install --upgrade faiss faiss-gpu | code |
122255862/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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_13 | [
"text_plain_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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
for cla, titanic_df in titanic_class:
print(cla)
... | code |
122255862/cell_9 | [
"text_html_output_2.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
print(f'People divided by gender in percentage: \n{titanic_gender}') | code |
122255862/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape | code |
122255862/cell_34 | [
"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
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor'... | code |
122255862/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic['avg_fare_class'] = titanic.groupby('Pclass')['... | code |
122255862/cell_33 | [
"text_plain_output_2.png",
"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
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor'... | code |
122255862/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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
print('Number of classes on board:')
titanic['Pclass'].nunique() | code |
122255862/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic['avg_fare_class'] = titanic.groupby('Pclass')['... | code |
122255862/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum() | code |
122255862/cell_19 | [
"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
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor'... | code |
122255862/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 |
122255862/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
print('How many people survived?')
titanic['Survived'].sum() | code |
122255862/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_32 | [
"text_plain_output_2.png",
"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
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic_gender = titanic['Sex'].value_counts(normalize=True)
wp = {'linewidth': 1, 'edgecolor'... | code |
122255862/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
print('People divided by gender:')
titanic['Sex'].value_counts() | code |
122255862/cell_15 | [
"text_plain_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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2) | code |
122255862/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)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.shape
titanic.groupby('Sex').Survived.sum()
titanic_class = titanic.groupby('Pclass')
titanic_class
titanic_class.get_group(1)
titanic_class.get_group(2)
... | code |
122255862/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('/kaggle/input/test-file/tested.csv')
titanic.head() | code |
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