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stringlengths 13
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sequencelengths 1
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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 entity or index into it. You can also get the text form of the whole entity, as though it were a single token.'
clean_text = clean(test)
clean_text_arr = np.array(clean_text.split())
clean_text_arr.shape | 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 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)
df = pd.DataFrame(columns=['text', 'label'])
old_dataset = pd.read_csv('./events.csv')
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
wnl = WordNetLemmatizer()
engstopwords = stopwords.words('english')
def lemmatize_all_types(word):
word = wnl.lemmatize(word, 'a')
word = wnl.lemmatize(word, 'v')
word = wnl.lemmatize(word, 'n')
return word
def clean(text):
text = re.sub('https?://\\w+\\.\\w+\\.\\w+', '', text).lower()
text = re.sub('[^a-zA-Z ]', '', text)
text = list(map(lemmatize_all_types, text.split()))
text = [word for word in text if word not in engstopwords]
text = ' '.join(text)
return text
df = pd.read_csv('../input/emails-events/emails_events.csv')
tfidf = TfidfVectorizer(max_features=10000)
dtm = tfidf.fit_transform(X).toarray()
words = tfidf.get_feature_names()
X_dtm = pd.DataFrame(columns=words, data=dtm)
model = MultinomialNB()
model.fit(X_train, y_train)
model.score(X_test, y_test)
text = 'can we have meeting on the next week please on morning'
text = clean(text)
enc = tfidf.transform([text])
model.predict(enc)
text = 'what a beautiful garden that we saw in the cinema'
text = clean(text)
enc = tfidf.transform([text])
model.predict(enc) | 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 Sunday'.lower().split()
for day in days:
page = requests.get('https://sentence.yourdictionary.com/saturday')
soup = BeautifulSoup(page.content, 'html.parser')
ps = soup.find_all('p', {'class': 'sentence-item__text'})
for p in ps:
df = df.append({'text': p.text, 'label': 1}, ignore_index=True)
days = 'Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.lower().split()
for day in days:
page = requests.get('https://sentence.yourdictionary.com/' + day)
soup = BeautifulSoup(page.content, 'html.parser')
ps = soup.find_all('p', {'class': 'sentence-item__text'})
for p in ps:
df = df.append({'text': p.text, 'label': 1}, ignore_index=True)
old_dataset.columns = ['text', 'label']
old_dataset.to_csv('good_dataset.csv', index=False)
months = 'January February March April May June July August September October November December'.lower().split()
for month in months:
page = requests.get('https://sentence.yourdictionary.com/' + month)
soup = BeautifulSoup(page.content, 'html.parser')
ps = soup.find_all('p', {'class': 'sentence-item__text'})
for p in ps:
df = df.append({'text': p.text, 'label': 1}, ignore_index=True)
for item in ['again']:
page = requests.get('https://sentence.yourdictionary.com/' + item)
soup = BeautifulSoup(page.content, 'html.parser')
ps = soup.find_all('p', {'class': 'sentence-item__text'})
for p in ps:
old_dataset = old_dataset.append({'text': p.text, 'label': 0}, ignore_index=True)
old_dataset.shape
old_dataset = pd.read_csv('./events.csv')
old_dataset.shape | 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 on january good ? in Cairo or Giza ?'
doc = nlp(email)
for ent in doc.ents:
print(ent.text, ent.label_) | 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_transform(X).toarray()
words = tfidf.get_feature_names()
X_dtm = pd.DataFrame(columns=words, data=dtm) | 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):
multiplier = 10 ** decimals
return int(n * multiplier) / multiplier
a = truncate(model2.similarity('risk', 'smoking'), 2)
b = truncate(model2.similarity('risk', 'heart'), 2)
c = truncate(model2.similarity('risk', 'pregnant'), 2)
d = truncate(model2.similarity('risk', 'cancer'), 2)
e = truncate(model2.similarity('risk', 'diabetes'), 2)
f = truncate(model2.similarity('risk', 'age'), 2)
g = truncate(model2.similarity('risk', 'asthma'), 2)
h = truncate(model2.similarity('risk', 'HIV'), 2)
i = truncate(model2.similarity('risk', 'transplant'), 2)
j = truncate(model2.similarity('risk', 'obesity'), 2)
k = truncate(model2.similarity('risk', 'immunocompromised'), 2)
l = truncate(model2.similarity('risk', 'underweight'), 2)
m = truncate(model2.similarity('risk', 'liver'), 2)
n = truncate(model2.similarity('risk', 'bronchitis'), 2)
o = truncate(model2.similarity('risk', 'COPD'), 2)
objects = ('Smoking', 'Heart Disease', 'Pregnancy', 'Cancer', 'Diabetes', 'Age', 'Asthma', 'HIV', 'Transplant', 'Obesity', 'Immunocompromised', 'Underweight', 'Liver Disease', 'Chronic Bronchitis', 'COPD')
y_pos = np.arange(len(objects))
similarity = [a, b, c, d, e, f, g, h, i, j, k, l, m, n, o]
fig = plt.figure(1, [15, 10])
axes = plt.gca()
axes.set_ylim([0, 1])
plt.bar(y_pos, similarity, align='center', alpha=0.5)
plt.xticks(y_pos, objects, rotation=90)
plt.ylabel('Cosine similarity with the word "risk" using Skip Gram model')
plt.title('Risk Factors')
count = -0.27
for i in similarity:
plt.text(count, i - 0.05, str(i))
count += 1
plt.show() | 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 warnings
warnings.filterwarnings(action='ignore') | 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 * multiplier) / multiplier
print('Cosine similarity using Skip Gram model between:')
a = truncate(model2.similarity('risk', 'smoking'), 2)
b = truncate(model2.similarity('risk', 'heart'), 2)
c = truncate(model2.similarity('risk', 'pregnant'), 2)
d = truncate(model2.similarity('risk', 'cancer'), 2)
e = truncate(model2.similarity('risk', 'diabetes'), 2)
f = truncate(model2.similarity('risk', 'age'), 2)
g = truncate(model2.similarity('risk', 'asthma'), 2)
h = truncate(model2.similarity('risk', 'HIV'), 2)
i = truncate(model2.similarity('risk', 'transplant'), 2)
j = truncate(model2.similarity('risk', 'obesity'), 2)
k = truncate(model2.similarity('risk', 'immunocompromised'), 2)
l = truncate(model2.similarity('risk', 'underweight'), 2)
m = truncate(model2.similarity('risk', 'liver'), 2)
n = truncate(model2.similarity('risk', 'bronchitis'), 2)
o = truncate(model2.similarity('risk', 'COPD'), 2)
print('risk and smoking : ', a)
print('risk and heart : ', b)
print('risk and pregnant : ', c)
print('risk and cancer : ', d)
print('risk and diabetes : ', e)
print('risk and age : ', f)
print('risk and asthma : ', g)
print('risk and HIV : ', h)
print('risk and transplant : ', i)
print('risk and obesity : ', j)
print('risk and immunocompromised : ', k)
print('risk and underweight : ', l)
print('risk and liver : ', m)
print('risk and bronchitis : ', n)
print('risk and COPD : ', o) | 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 = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')
df.shape
df.describe().T
df.isnull().sum()
from sklearn.metrics import classification_report, confusion_matrix
xgb = XGBClassifier()
xgb.fit(x_train, y_train)
y_pred = xgb.predict(x_test)
accuracy1 = xgb.score(x_test, y_test)
cm = confusion_matrix(y_test, y_pred)
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
des_class = DecisionTreeClassifier()
des_class.fit(x_train, y_train)
des_predict = des_class.predict(x_test)
print(classification_report(y_test, des_predict))
accuracy3 = des_class.score(x_test, y_test)
print(accuracy3 * 100, '%')
cm = confusion_matrix(y_test, des_predict)
sns.heatmap(cm, annot=True) | 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 accuracy_score
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | 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': 8, 'MDVP:Shimmer': 9, 'MDVP:Shimmer(dB)': 10, 'Shimmer:APQ3': 11, 'Shimmer:APQ5': 12, 'MDVP:APQ': 13, 'Shimmer:DDA': 14, 'NHR': 15, 'HNR': 16, 'RPDE': 17, 'DFA': 18, 'spread1': 19, 'spread2': 20, 'D2': 21, 'PPE': 22}
q1 = df.quantile(0.25)
q2 = df.quantile(0.5)
q3 = df.quantile(0.75)
IQR = q3 - q1
df_out = df[~((df < q1 - 1.5 * IQR) | (df > q3 + 1.5 * IQR)).any(axis=1)]
plt.figure(figsize=(20, 30))
for variable, i in col.items():
plt.subplot(5, 5, i)
plt.boxplot(df_out[variable])
plt.title(variable)
plt.show() | 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 DecisionTreeClassifier
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.csv')
df.shape
df.describe().T
df.isnull().sum()
from sklearn.metrics import classification_report, confusion_matrix
xgb = XGBClassifier()
xgb.fit(x_train, y_train)
y_pred = xgb.predict(x_test)
accuracy1 = xgb.score(x_test, y_test)
cm = confusion_matrix(y_test, y_pred)
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
des_class = DecisionTreeClassifier()
des_class.fit(x_train, y_train)
des_predict = des_class.predict(x_test)
accuracy3 = des_class.score(x_test, y_test)
cm = confusion_matrix(y_test, des_predict)
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report, confusion_matrix
nvclassifier = GaussianNB()
nvclassifier.fit(x_train, y_train)
y_pred = nvclassifier.predict(x_test)
print(classification_report(y_test, y_pred))
print(accuracy_score(y_pred, y_test) * 100, '%')
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True) | 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.csv')
df.shape
df.describe().T
df.isnull().sum()
from sklearn.metrics import classification_report, confusion_matrix
xgb = XGBClassifier()
xgb.fit(x_train, y_train)
y_pred = xgb.predict(x_test)
print(classification_report(y_test, y_pred))
accuracy1 = xgb.score(x_test, y_test)
print(accuracy1 * 100, '%')
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True) | 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': 8, 'MDVP:Shimmer': 9, 'MDVP:Shimmer(dB)': 10, 'Shimmer:APQ3': 11, 'Shimmer:APQ5': 12, 'MDVP:APQ': 13, 'Shimmer:DDA': 14, 'NHR': 15, 'HNR': 16, 'RPDE': 17, 'DFA': 18, 'spread1': 19, 'spread2': 20, 'D2': 21, 'PPE': 22}
plt.figure(figsize=(20, 30))
for variable, i in col.items():
plt.subplot(5, 5, i)
plt.boxplot(df[variable])
plt.title(variable)
plt.show() | 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 %)', data=df) | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
sns.boxplot(data=df, x='Oldpeak') | 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'
fig, axs = plt.subplots(6, 2, figsize=(15, 28))
i = 1
for feature in df.columns:
if feature not in ['HeartDisease'] and i < 14:
plt.subplot(6, 2, i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i += 1 | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15, 28))
i = 1
for feature in df.columns:
if feature not in ['HeartDisease'] and i < 14:
plt.subplot(6, 2, i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i += 1 | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
sns.boxplot(data=df, x='RestingBP') | 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_adjust(hspace=0.2)
color = 'winter'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
labels = ['Less chance of heart attack', 'More chance of heart attack']
values = [df[df['HeartDisease'] == 1].count().to_numpy()[0], df[df['HeartDisease'] == 0].count().to_numpy()[0]]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, marker_colors=['cyan', 'darkblue'], textinfo='label+percent')])
fig.update(layout_title_text='Chance of heart attack', layout_showlegend=False)
fig.show() | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df | 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(hspace=0.2)
color = 'winter'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
px.scatter(data_frame=df, x='Cholesterol', y='MaxHR', color='HeartDisease') | 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(hspace=0.2)
color = 'winter'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
px.scatter(data_frame=df, x='Age', y='MaxHR', color='HeartDisease') | 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 = ['Sex', 'FastingBS', 'ExerciseAngina']
df.isna().all()
df[continuos_f].describe() | 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(hspace=0.2)
color = 'winter'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
px.scatter(data_frame=df, x='RestingBP', y='MaxHR', color='HeartDisease') | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False) | 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'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
sns.boxplot(data=df, x='Cholesterol') | 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(hspace=0.2)
color = 'winter'
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
df_corr = df.corr()
df_corr['HeartDisease'].sort_values(ascending=False)
def delete_outliers(label=None):
Q1 = df[label].quantile(0.25)
Q3 = df[label].quantile(0.75)
IQR = Q3 - Q1
df_ch_outliers = df[~((df[label] > Q1 - 1.5 * IQR) & (df[label] < Q3 + 1.5 * IQR))]
return df.drop(df_ch_outliers.index)
df = delete_outliers('Cholesterol')
df
fig, axs = plt.subplots(6, 2, figsize=(15,28))
i=1
for feature in df.columns:
if feature not in ["HeartDisease"] and i < 14:
plt.subplot(6,2,i)
sns.histplot(data=df, x=feature, kde=True, palette=color, hue='HeartDisease')
i+=1
px.scatter(data_frame=df, x='Oldpeak', y='MaxHR', color='HeartDisease') | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()] | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]] | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]]
transactions.drop([0, 2, 5], axis=0)
transactions[:3]
transactions[3:]
transactions.tail(-2) | 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', 'UserID': 'UID'})
transactions[['ProductID', 'Quantity', 'TransactionDate', 'TransactionID', 'UserID']] | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]]
transactions.drop([0, 2, 5], axis=0)
transactions[:3]
transactions[3:]
transactions.tail(2) | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]]
transactions.drop([0, 2, 5], axis=0) | 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', 'UserID': 'UID'}) | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]]
transactions.drop([0, 2, 5], axis=0)
transactions[:3]
transactions[3:]
transactions.tail(-3) | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
col = 'ProductID'
transactions[[col]].values[:, 0] | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2] | 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', 'UserID': 'UID'})
transactions[pd.unique(['UserID'] + transactions.columns.values.tolist()).tolist()]
transactions.iloc[:, 2]
transactions.ProductID.values
transactions.iloc[[0, 2, 5]]
transactions.drop([0, 2, 5], axis=0)
transactions[:3]
transactions.head(3) | 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(how='all')
df.info() | 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(how='all')
df['status'].value_counts() | 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(how='all')
df['status'].value_counts() | 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)
papers_df = pd.DataFrame(columns=['paper_id', 'title', 'authors', 'abstract', 'body_text', 'bib_titles', 'dataset'])
authors_df = pd.DataFrame(columns=['author', 'affiliation'])
affiliations_df = pd.DataFrame(columns=['affiliation', 'country'])
bib_entries_df = pd.DataFrame(columns=['title', 'authors', 'year', 'venue'])
line_author_df = 0
line_affiliations_df = 0
line_bib_entries_df = 0
for line, file in enumerate(files):
n_files = len(files)
file_path = os.path.join('../data/{}'.format(dir), file)
with open(file_path) as f:
data = json.load(f)
paper_id = data['paper_id']
title = data['metadata']['title']
authors, affiliations, countries = ('', '', '')
for author in data['metadata']['authors']:
first_last_name = author['first'] + ' ' + author['last']
authors = authors + ' || ' + first_last_name
if author['affiliation'] == {}:
affiliation = 'NA'
affiliations = affiliations + ' || ' + affiliation.strip()
country = 'NA'
countries = countries + ' || ' + country.strip()
continue
affiliation = author['affiliation']['laboratory'] + ' ' + author['affiliation']['institution']
affiliations = affiliations + ' || ' + affiliation.strip()
if 'country' not in author['affiliation']['location'].keys():
country = 'NA'
countries = countries + ' || ' + country
continue
country = author['affiliation']['location']['country']
countries = countries + ' || ' + country
authors = authors[4:]
affiliations = affiliations[4:]
countries = countries[4:]
abstract = ''
for info in data['abstract']:
abstract = abstract + ' ' + info['text']
abstract = abstract.strip()
body_text = ''
for info in data['body_text']:
body_text = body_text + ' ' + info['text']
body_text = body_text.strip()
bib_titles, bib_authors, years, venues = ('', '', '', '')
for bib in data['bib_entries']:
bib_titles = bib_titles + ' || ' + data['bib_entries'][bib]['title']
year = data['bib_entries'][bib]['year']
years = years + ' || ' + str(year)
venue = data['bib_entries'][bib]['venue']
venues = venues + ' || ' + venue
bib_author = [author['first'] + ' ' + author['last'] for author in data['bib_entries'][bib]['authors']]
bib_author = ' | '.join(bib_author)
bib_authors = bib_authors + ' || ' + bib_author
bib_titles, bib_authors, years, venues = (bib_titles[4:], bib_authors[4:], years[4:], venues[4:])
papers_df.loc[line, :] = [paper_id, title, authors, abstract, body_text, bib_titles, dir]
authors_list = authors.split(' || ')
affiliations_list = affiliations.split(' || ')
for i in range(len(authors_list)):
authors_df.loc[line_author_df, :] = (authors_list[i], affiliations_list[i])
line_author_df += 1
countries_list = countries.split(' || ')
for i in range(len(affiliations_list)):
affiliations_df.loc[line_affiliations_df, :] = (affiliations_list[i], countries_list[i])
line_affiliations_df += 1
bib_titles_list = bib_titles.split(' || ')
bib_authors_list = bib_authors.split(' || ')
years_list = years.split(' || ')
venues_list = venues.split(' || ')
for i in range(len(bib_titles_list)):
bib_entries_df.loc[line_bib_entries_df, :] = (bib_titles_list[i], bib_authors_list[i], years_list[i], venues_list[i])
line_bib_entries_df += 1
authors_df = authors_df.drop_duplicates().reset_index(drop=True)
affiliations_df = affiliations_df.drop_duplicates().reset_index(drop=True)
bib_entries_df = bib_entries_df.drop_duplicates().reset_index(drop=True)
return (papers_df, authors_df, affiliations_df, bib_entries_df)
df = pd.read_csv('/kaggle/input/papers/papers.csv', sep=';', nrows=100)
df = df.drop_duplicates()
df.shape | 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)
papers_df = pd.DataFrame(columns=['paper_id', 'title', 'authors', 'abstract', 'body_text', 'bib_titles', 'dataset'])
authors_df = pd.DataFrame(columns=['author', 'affiliation'])
affiliations_df = pd.DataFrame(columns=['affiliation', 'country'])
bib_entries_df = pd.DataFrame(columns=['title', 'authors', 'year', 'venue'])
line_author_df = 0
line_affiliations_df = 0
line_bib_entries_df = 0
for line, file in enumerate(files):
n_files = len(files)
file_path = os.path.join('../data/{}'.format(dir), file)
with open(file_path) as f:
data = json.load(f)
paper_id = data['paper_id']
title = data['metadata']['title']
authors, affiliations, countries = ('', '', '')
for author in data['metadata']['authors']:
first_last_name = author['first'] + ' ' + author['last']
authors = authors + ' || ' + first_last_name
if author['affiliation'] == {}:
affiliation = 'NA'
affiliations = affiliations + ' || ' + affiliation.strip()
country = 'NA'
countries = countries + ' || ' + country.strip()
continue
affiliation = author['affiliation']['laboratory'] + ' ' + author['affiliation']['institution']
affiliations = affiliations + ' || ' + affiliation.strip()
if 'country' not in author['affiliation']['location'].keys():
country = 'NA'
countries = countries + ' || ' + country
continue
country = author['affiliation']['location']['country']
countries = countries + ' || ' + country
authors = authors[4:]
affiliations = affiliations[4:]
countries = countries[4:]
abstract = ''
for info in data['abstract']:
abstract = abstract + ' ' + info['text']
abstract = abstract.strip()
body_text = ''
for info in data['body_text']:
body_text = body_text + ' ' + info['text']
body_text = body_text.strip()
bib_titles, bib_authors, years, venues = ('', '', '', '')
for bib in data['bib_entries']:
bib_titles = bib_titles + ' || ' + data['bib_entries'][bib]['title']
year = data['bib_entries'][bib]['year']
years = years + ' || ' + str(year)
venue = data['bib_entries'][bib]['venue']
venues = venues + ' || ' + venue
bib_author = [author['first'] + ' ' + author['last'] for author in data['bib_entries'][bib]['authors']]
bib_author = ' | '.join(bib_author)
bib_authors = bib_authors + ' || ' + bib_author
bib_titles, bib_authors, years, venues = (bib_titles[4:], bib_authors[4:], years[4:], venues[4:])
papers_df.loc[line, :] = [paper_id, title, authors, abstract, body_text, bib_titles, dir]
authors_list = authors.split(' || ')
affiliations_list = affiliations.split(' || ')
for i in range(len(authors_list)):
authors_df.loc[line_author_df, :] = (authors_list[i], affiliations_list[i])
line_author_df += 1
countries_list = countries.split(' || ')
for i in range(len(affiliations_list)):
affiliations_df.loc[line_affiliations_df, :] = (affiliations_list[i], countries_list[i])
line_affiliations_df += 1
bib_titles_list = bib_titles.split(' || ')
bib_authors_list = bib_authors.split(' || ')
years_list = years.split(' || ')
venues_list = venues.split(' || ')
for i in range(len(bib_titles_list)):
bib_entries_df.loc[line_bib_entries_df, :] = (bib_titles_list[i], bib_authors_list[i], years_list[i], venues_list[i])
line_bib_entries_df += 1
authors_df = authors_df.drop_duplicates().reset_index(drop=True)
affiliations_df = affiliations_df.drop_duplicates().reset_index(drop=True)
bib_entries_df = bib_entries_df.drop_duplicates().reset_index(drop=True)
return (papers_df, authors_df, affiliations_df, bib_entries_df)
df = pd.read_csv('/kaggle/input/papers/papers.csv', sep=';', nrows=100)
meta_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv')
meta_df.head() | 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)
papers_df = pd.DataFrame(columns=['paper_id', 'title', 'authors', 'abstract', 'body_text', 'bib_titles', 'dataset'])
authors_df = pd.DataFrame(columns=['author', 'affiliation'])
affiliations_df = pd.DataFrame(columns=['affiliation', 'country'])
bib_entries_df = pd.DataFrame(columns=['title', 'authors', 'year', 'venue'])
line_author_df = 0
line_affiliations_df = 0
line_bib_entries_df = 0
for line, file in enumerate(files):
n_files = len(files)
file_path = os.path.join('../data/{}'.format(dir), file)
with open(file_path) as f:
data = json.load(f)
paper_id = data['paper_id']
title = data['metadata']['title']
authors, affiliations, countries = ('', '', '')
for author in data['metadata']['authors']:
first_last_name = author['first'] + ' ' + author['last']
authors = authors + ' || ' + first_last_name
if author['affiliation'] == {}:
affiliation = 'NA'
affiliations = affiliations + ' || ' + affiliation.strip()
country = 'NA'
countries = countries + ' || ' + country.strip()
continue
affiliation = author['affiliation']['laboratory'] + ' ' + author['affiliation']['institution']
affiliations = affiliations + ' || ' + affiliation.strip()
if 'country' not in author['affiliation']['location'].keys():
country = 'NA'
countries = countries + ' || ' + country
continue
country = author['affiliation']['location']['country']
countries = countries + ' || ' + country
authors = authors[4:]
affiliations = affiliations[4:]
countries = countries[4:]
abstract = ''
for info in data['abstract']:
abstract = abstract + ' ' + info['text']
abstract = abstract.strip()
body_text = ''
for info in data['body_text']:
body_text = body_text + ' ' + info['text']
body_text = body_text.strip()
bib_titles, bib_authors, years, venues = ('', '', '', '')
for bib in data['bib_entries']:
bib_titles = bib_titles + ' || ' + data['bib_entries'][bib]['title']
year = data['bib_entries'][bib]['year']
years = years + ' || ' + str(year)
venue = data['bib_entries'][bib]['venue']
venues = venues + ' || ' + venue
bib_author = [author['first'] + ' ' + author['last'] for author in data['bib_entries'][bib]['authors']]
bib_author = ' | '.join(bib_author)
bib_authors = bib_authors + ' || ' + bib_author
bib_titles, bib_authors, years, venues = (bib_titles[4:], bib_authors[4:], years[4:], venues[4:])
papers_df.loc[line, :] = [paper_id, title, authors, abstract, body_text, bib_titles, dir]
authors_list = authors.split(' || ')
affiliations_list = affiliations.split(' || ')
for i in range(len(authors_list)):
authors_df.loc[line_author_df, :] = (authors_list[i], affiliations_list[i])
line_author_df += 1
countries_list = countries.split(' || ')
for i in range(len(affiliations_list)):
affiliations_df.loc[line_affiliations_df, :] = (affiliations_list[i], countries_list[i])
line_affiliations_df += 1
bib_titles_list = bib_titles.split(' || ')
bib_authors_list = bib_authors.split(' || ')
years_list = years.split(' || ')
venues_list = venues.split(' || ')
for i in range(len(bib_titles_list)):
bib_entries_df.loc[line_bib_entries_df, :] = (bib_titles_list[i], bib_authors_list[i], years_list[i], venues_list[i])
line_bib_entries_df += 1
authors_df = authors_df.drop_duplicates().reset_index(drop=True)
affiliations_df = affiliations_df.drop_duplicates().reset_index(drop=True)
bib_entries_df = bib_entries_df.drop_duplicates().reset_index(drop=True)
return (papers_df, authors_df, affiliations_df, bib_entries_df)
df = pd.read_csv('/kaggle/input/papers/papers.csv', sep=';', nrows=100)
df.head() | 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$)', 'Spending Score (1-100)']:
n += 1
df.columns | code |
73065927/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.head() | code |
73065927/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
n = 0
for x in ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']:
n += 1
sns.heatmap(df.corr()) | 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$)', 'Spending Score (1-100)']:
n += 1
df.columns
x = df[''] | code |
73065927/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.dtypes
df.isnull().sum()
plt.figure(1, figsize=(15, 6))
n = 0
for x in ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']:
n += 1
plt.subplot(1, 3, n)
sns.distplot(df[x], bins=20)
plt.title('Distplot of {}'.format(x))
plt.show() | 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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