path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105194628/cell_3 | [
"text_plain_output_1.png"
] | place = input('1. Hill Station, 2. Beach Enter he place you would like to visit')
if place == '1' or 'Hill Station':
print('Let us! Plan our trip to Hill Station now')
elif place == '2' or 'Beach':
print('Let us pack our bags for the Sun View') | code |
105194628/cell_5 | [
"text_plain_output_1.png"
] | ask = input('We are planning to go to vist an Hill Station or a temple would you like to join us? ')
if ask == 'Yes' or ask == 'yes':
ask2 = input('Do you mind to pick a place for our visit, We are thinking about Beach and Temple ')
if ask2 == 'Beach' or ask2 == 'beach':
print('Let us pack our bags for... | code |
74050036/cell_13 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_25 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_34 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = jso... | code |
74050036/cell_23 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_30 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = jso... | code |
74050036/cell_29 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = jso... | code |
74050036/cell_11 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_19 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_15 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_17 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_24 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_10 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_27 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.js... | code |
74050036/cell_36 | [
"text_html_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = jso... | code |
34125586/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import requests
import json
import pandas as pd
import time
import plotly
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 |
34125586/cell_18 | [
"image_output_1.png"
] | import json
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 requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.rio... | code |
34125586/cell_8 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url +... | code |
34125586/cell_16 | [
"image_output_1.png"
] | import json
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 requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.rio... | code |
34125586/cell_14 | [
"image_output_1.png"
] | import json
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 requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.rio... | code |
34125586/cell_10 | [
"text_plain_output_1.png"
] | import json
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 requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.rio... | code |
34125586/cell_12 | [
"text_plain_output_1.png"
] | import json
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 requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.rio... | code |
121150743/cell_5 | [
"text_plain_output_1.png"
] | pip install Keras-Preprocessing | code |
128026093/cell_25 | [
"image_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = ... | code |
128026093/cell_30 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
impor... | code |
128026093/cell_20 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().... | code |
128026093/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128026093/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = ... | code |
128026093/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128026093/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128026093/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
impor... | code |
128026093/cell_8 | [
"image_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128026093/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128026093/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128026093/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128026093/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128020724/cell_2 | [
"image_output_1.png"
] | pip install fuzzy-c-means | code |
128020724/cell_11 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.metrics import adjusted_rand_score
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = min... | code |
128020724/cell_19 | [
"text_html_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np... | code |
128020724/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X, feature_range=(0, 1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
targe... | code |
128020724/cell_18 | [
"text_plain_output_1.png"
] | ax = plt.subplot(2,3,1)
ax.imshow(np.reshape(X[0],[npixels,npixels]),cmap='Greys')
ax.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax = plt.subplot(2,3,3)
ax.plot(np.log(fcm.soft_predict(X[[0]])[0]));
ax.grid(visible=True)
ax.set_xticks(list(range(10)))
ax.set(xlabel='La... | code |
128020724/cell_16 | [
"image_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np... | code |
128020724/cell_3 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
from matplotlib import pyplot as plt
from fcmeans import FCM
from sklearn.metrics import adjusted_rand_score
from pandas import crosstab | code |
128020724/cell_14 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np... | code |
128020724/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fcmeans import FCM
from pandas import crosstab
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_... | code |
128020724/cell_5 | [
"image_output_1.png"
] | from sklearn.datasets import load_digits
digits = load_digits()
print(digits.DESCR) | code |
128031562/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import nltk
nltk.download('punkt') | code |
128031562/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install newspaper3k | code |
128031562/cell_1 | [
"text_plain_output_1.png"
] | !pip install transformers torch | code |
128031562/cell_10 | [
"text_plain_output_1.png"
] | from newspaper import Article
from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer, pipeline
from urllib.request import urlopen
import feedparser
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-cnndm')
tokenizer = ProphetNetTokenizer.from_pr... | code |
128031562/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer, pipeline
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-cnndm')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-cnndm') | code |
128041917/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
import seaborn as sns
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
fig,... | code |
128041917/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a | code |
128041917/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes | code |
128041917/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a | code |
128041917/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex']... | code |
128041917/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.tail() | code |
128041917/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import cufflinks as cf
import plotly.offline as py
color = sns.color_palette()
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import plotly.tools as tls
import warnings
warnings... | code |
128041917/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex']... | code |
128041917/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape | code |
128041917/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8)) = plt.subplots(... | code |
128041917/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe | code |
128041917/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a_sex = a['Sex'].value_counts()
print(a_sex)
a['Sex'].value_counts().plot(kind='bar') | code |
128041917/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a.info() | code |
128041917/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex']... | code |
128041917/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum() | code |
128041917/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.head() | code |
128025497/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import chi2
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kagg... | code |
128025497/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
print('Number of missing values in each column:')
print(train_data.isnull().sum())
print('Number of duplicate rows:... | code |
128025497/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils.class_weight import compute_sample_weight
import matplotlib.pyplot as plt
import numpy as np
import pandas as p... | code |
128025497/cell_2 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
print(train_data.sample(5)) | code |
128025497/cell_19 | [
"image_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils.class_weight import compute_sample_weight
import matplotlib.pyplot as plt
import numpy as np
import pandas as p... | code |
128025497/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
plt.xticks(rotation=90)
train_data['symptom_sum'] = train_data.iloc[:, 1:-1].sum(axis=1)
plt.... | code |
128025497/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop = True)
print(len(X.columns))
significant_features = [score[0] for score in sorted_scores if score[1] < 0.05]
X_significant = X[significant_features]
print(len(X_significant.columns))
X_scaled = scaler.fit_transform(X_significant)""" | code |
104129266/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols | code |
104129266/cell_6 | [
"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)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[... | code |
104129266/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.info() | code |
104129266/cell_7 | [
"text_html_output_1.png"
] | import category_encoders as ce
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_c... | code |
104129266/cell_8 | [
"text_html_output_1.png"
] | import category_encoders as ce
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_c... | code |
104129266/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0) | code |
104129266/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_cols + ['failure']]
training | code |
33099008/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/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 |
33099008/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
vodafone_subset_6.head(10) | code |
33099008/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33099008/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
34128312/cell_4 | [
"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/youtube-new/USvideos.csv')
print(df.columns) | code |
34128312/cell_6 | [
"text_plain_output_1.png",
"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/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(['category_id']).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128312/cell_7 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"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/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_ca... | code |
34128312/cell_5 | [
"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/youtube-new/USvideos.csv')
df.head() | code |
122260425/cell_21 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_9 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum() | code |
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