path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122262215/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()
model.fit(X_train, y_train)
model.score(X_test, y_test) | code |
122262215/cell_7 | [
"text_plain_output_1.png"
] | import re
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 10:00 am ok on january good ? in Cairo or Giza ?'
email = email.lower()
re.findall('saturday|sunday|monday|wednesday', email)
re.findall('january|february', email) | code |
122262215/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
test = 'The standard way to access entity annotations is the doc.ents property, which produces a sequence of Span objects. The entity type is accessible either as a hash value or as a string using the attributes ent.label and The Span object acts as a sequence of tokens so you can iterate over the ... | code |
122262215/cell_8 | [
"text_plain_output_1.png"
] | import re
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 10:00 am ok on january good ? in Cairo or Giza ?'
email = email.lower()
re.findall('saturday|sunday|monday|wednesday', email)
re.findall('january|february', email)
re.findall('\\d{1,2}:\\d{1,2} a?p?m', email) | code |
122262215/cell_43 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import pandas as pd
import pandas as pd
import re
import re
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 1... | code |
122262215/cell_24 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
ps = soup.find_all('p', {'class': 'sentence-item__text'})
df = pd.DataFrame(columns=['text', 'label'])
days = 'Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.lower().split()
days
days = 'Monday Tuesday Wednesday Thursday Friday Saturday ... | code |
122262215/cell_10 | [
"text_plain_output_1.png"
] | import spacy
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 10:00 am ok on january good ? in Cairo or Giza ?'
email = email.lower()
import spacy
nlp = spacy.load('en_core_web_sm')
email = 'We are goind to meet on 2025 1919 saturday or sunday on 09:30 PM or 10:00 in New York or Florida am ok... | code |
122262215/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.DataFrame(columns=['text', 'label'])
old_dataset = pd.read_csv('./events.csv')
df = pd.read_csv('../input/emails-events/emails_events.csv')
tfidf = TfidfVectorizer(max_features=10000)
dtm = tfidf.fit_transf... | code |
34134627/cell_28 | [
"text_plain_output_1.png"
] | import csv
import gensim
import matplotlib.pyplot as plt
import numpy as np
with open('../input/tokenized-words-cord19-challenge/data.csv', newline='') as f:
reader = csv.reader(f)
data = list(reader)
model2 = gensim.models.Word2Vec(data, min_count=1, size=100, window=5, sg=1)
def truncate(n, decimals=0):... | code |
34134627/cell_3 | [
"image_output_1.png"
] | import nltk
import warnings
from tqdm.notebook import tqdm
import csv
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize, word_tokenize
from gensim.models import Word2Vec
import gensim
import os
import json
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import... | code |
34134627/cell_27 | [
"text_plain_output_1.png"
] | import csv
import gensim
with open('../input/tokenized-words-cord19-challenge/data.csv', newline='') as f:
reader = csv.reader(f)
data = list(reader)
model2 = gensim.models.Word2Vec(data, min_count=1, size=100, window=5, sg=1)
def truncate(n, decimals=0):
multiplier = 10 ** decimals
return int(n * m... | code |
50224594/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df.columns | code |
50224594/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df['status'].value_counts() | code |
50224594/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape | code |
50224594/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df =... | code |
50224594/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T | code |
50224594/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from sklearn.metrics import... | code |
50224594/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
sns.catplot(x='status', kind='count', data=df) | code |
50224594/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum() | code |
50224594/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df.columns
col = {'MDVP:Fo(Hz)': 1, 'MDVP:Fhi(Hz)': 2, 'MDVP:Flo(Hz)': 3, 'MDVP:Jitter(%)': 4, 'MDVP:Jitter(Abs)': 5, 'MDVP:RAP': 6, 'MDVP:PPQ': 7, 'Jitter:DDP... | code |
50224594/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClass... | code |
50224594/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, confusion_matrix
from xgboost import XGBClassifier
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.... | code |
50224594/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df.hist(figsize=(20, 12))
plt.show() | code |
50224594/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df.columns
col = {'MDVP:Fo(Hz)': 1, 'MDVP:Fhi(Hz)': 2, 'MDVP:Flo(Hz)': 3, 'MDVP:Jitter(%)': 4, 'MDVP:Jitter(Abs)': 5, 'MDVP:RAP': 6, 'MDVP:PPQ': 7, 'Jitter:DDP... | code |
50224594/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
df.columns
q1 = df.quantile(0.25)
q2 = df.quantile(0.5)
q3 = df.quantile(0.75)
IQR = q3 - q1
print(IQR) | code |
50224594/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.head() | code |
50224594/cell_10 | [
"text_plain_output_1.png"
] | percentage_of_disease = 147 / (147 + 48) * 100
percentage_of_not_having_disease = 48 / (147 + 48) * 100
print('percentage of having disease', percentage_of_disease)
print('percentage of not having disease', percentage_of_not_having_disease) | code |
50224594/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.info() | code |
105180183/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/gdp-growth-of-pakistan/GDP Growth of Pakistan - Sheet1 (1).csv')
columns = ['Year', 'GDP growth(annual %)']
df.columns = columns
df = df[3:]
sns.regplot(x='Year', y='GDP growth(annual %)', ... | code |
105180183/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/gdp-growth-of-pakistan/GDP Growth of Pakistan - Sheet1 (1).csv')
columns = ['Year', 'GDP growth(annual %)']
df.columns = columns
df = df[3:]
df.head() | code |
105180183/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/gdp-growth-of-pakistan/GDP Growth of Pakistan - Sheet1 (1).csv')
df.head() | code |
105180183/cell_11 | [
"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/gdp-growth-of-pakistan/GDP Growth of Pakistan - Sheet1 (1).csv')
columns = ['Year', 'GDP growth(annual %)']
df.columns = columns
df = df[3:]
df.info() | code |
105180183/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 |
122249667/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_13 | [
"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
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use('seaborn')
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_4 | [
"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/heart-failure-prediction/heart.csv')
df.head() | code |
122249667/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_30 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_... | code |
122249667/cell_20 | [
"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
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_29 | [
"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 plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust... | code |
122249667/cell_26 | [
"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 plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust... | code |
122249667/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
continuos_f = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
categorical_f = ['ChestPainType', 'RestingECG', 'ST_Slope']
binaries_f = ['S... | code |
122249667/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 |
122249667/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates() | code |
122249667/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust... | code |
122249667/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.info() | code |
122249667/cell_15 | [
"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
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust(hspace=0.2)
color = 'winter'... | code |
122249667/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all() | code |
122249667/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')
df.shape
df.drop_duplicates()
df.isna().all()
plt.style.use("seaborn")
plt.subplots_adjust... | code |
122249667/cell_5 | [
"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/heart-failure-prediction/heart.csv')
df.shape | code |
2011514/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values | code |
2011514/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values | code |
2011514/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0] | code |
2011514/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1] | code |
2011514/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count() | code |
2011514/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True) | code |
2011514/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index | code |
2011514/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.shape[0]
transactions.shape[1]
transactions.index
transactions.index.values
transactions.columns.values
transactions.count()
transactions.sum(skipna=True, numeric_only=True)
transactions.rename(columns={'ProductID': 'PID',... | code |
2011514/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
transactions = pd.read_csv('../input/transactions.csv')
transactions.info() | code |
129032387/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
df.info() | code |
129032387/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df.info() | code |
129032387/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False) | code |
129032387/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 |
129032387/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df.dropna(ho... | code |
129032387/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df.dropna(ho... | code |
129032387/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
print(df.head(2)) | code |
129032387/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/kaggle/input/pakistans-largest-ecommerce-dataset/Pakistan Largest Ecommerce Dataset.csv', low_memory=False)
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df.dropna(ho... | code |
32068380/cell_20 | [
"image_output_1.png"
] | from IPython.display import Image
Image('../input/images-search-engine-cord19/abstract.PNG') | code |
32068380/cell_18 | [
"image_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
Image('../input/images-search-engine-cord19/results.PNG') | code |
32068380/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
def get_data(dir_path):
"""
Take as input a directory path containing json files from
biorxiv_medrxiv, comm_use_subset, noncomm_use_subset or custom_license.
Four dataframes are returned: papers_df, authors_df, affiliations_df, bib_entries_df
"""
files = os.listdir(dir_path... | code |
32068380/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
def get_data(dir_path):
"""
Take as input a directory path containing json files from
biorxiv_medrxiv, comm_use_subset, noncomm_use_subset or custom_license.
Four dataframes are returned: papers_df, authors_df, affiliations_df, bib_entries_df
"""
files = os.listdir(dir_path... | code |
32068380/cell_24 | [
"image_output_1.png"
] | from IPython.display import Image
Image('../input/images-search-engine-cord19/sentences.PNG') | code |
32068380/cell_22 | [
"image_output_1.png"
] | from IPython.display import Image
Image('../input/images-search-engine-cord19/sentences.PNG') | code |
32068380/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
def get_data(dir_path):
"""
Take as input a directory path containing json files from
biorxiv_medrxiv, comm_use_subset, noncomm_use_subset or custom_license.
Four dataframes are returned: papers_df, authors_df, affiliations_df, bib_entries_df
"""
files = os.listdir(dir_path... | code |
1010064/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_json('../input/train.json', typ='frame')
test_df = pd.read_json('../input/test.json', typ='frame')
print(train_df.shape)
print('----------')
print(test_df.shape) | code |
1010064/cell_1 | [
"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 |
1010064/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_json('../input/train.json', typ='frame')
test_df = pd.read_json('../input/test.json', typ='frame')
train_df.head() | code |
1010064/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_json('../input/train.json', typ='frame')
test_df = pd.read_json('../input/test.json', typ='frame')
train_df.info()
print('-------------------')
test_df.info() | code |
73065927/cell_13 | [
"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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
n = 0
for x in ['Age', 'Annual Income (k$)', 'Spend... | code |
73065927/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.head() | code |
73065927/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
n = 0
for x in ['Age', 'Annual Income (k$)', 'Spend... | code |
73065927/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 |
73065927/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)
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes | code |
73065927/cell_8 | [
"application_vnd.jupyter.stderr_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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum() | code |
73065927/cell_14 | [
"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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
n = 0
for x in ['Age', 'Annual Income (k$)', 'Spend... | code |
73065927/cell_10 | [
"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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
plt.figure(1, figsize=(15, 6))
n = 0
for x in ['Age... | code |
73065927/cell_5 | [
"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)
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.describe() | code |
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