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Good Grasshopper answered on November 21, 2021 Popularity 7/10 Helpfulness 1/10

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import os
import io
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

import warnings
warnings.filterwarnings('ignore')

def readFiles(path):
    for root, dirnames, filenames in os.walk(path):
        for filename in filenames:
            path = os.path.join(root, filename)

            inBody = False
            lines = []
            f = io.open(path, 'r', encoding='latin1')
            for line in f:
                if inBody:
                    lines.append(line)
                elif line == '\n':
                    inBody = True
            f.close()
            message = '\n'.join(lines)
            yield path, message


def dataFrameFromDirectory(path, classification):
    rows = []
    index = []
    for filename, message in readFiles(path):
        rows.append({'message': message, 'class': classification})
        index.append(filename)

    return DataFrame(rows, index=index)

data = DataFrame({'message': [], 'class': []})

data = data.append(dataFrameFromDirectory('email-messages/spam', 'spam'))
data = data.append(dataFrameFromDirectory('email-messages/not-spam', 'not-spam'))

Popularity 7/10 Helpfulness 1/10 Language python

Source: bobbecket.github.io

Tags: pandas python

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Contributed on Feb 14 2024

Good Grasshopper

0 Answers Avg Quality 2/10

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import pandas as pd 
 
# Creating a dictionary to store data
data = {'Name':['Tony', 'Steve', 'Bruce', 'Peter' ],
        'Age': [35, 70, 45, 20] } 
 
# Creating DataFrame 
df = pd.DataFrame(data) 
 
# Print the dataframe
df

Popularity 10/10 Helpfulness 3/10 Language python

Source: www.geeksforgeeks.org

Tags: dataframe data

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Contributed on Nov 13 2022

Sirada Boonchuay

0 Answers Avg Quality 2/10

pandas dataframe

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d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Popularity 10/10 Helpfulness 2/10 Language python

Source: pandas.pydata.org

Tags: dataframe data

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Contributed on Nov 21 2021

Nutty Newt

0 Answers Avg Quality 2/10

Pandas DataFrame

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import pandas as pd

data = {
	"calories" : [420, 380, 390],
    "duration" : [50, 40, 45]
}

df = pd.DataFrame(data)
print(df)

Popularity 10/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: dataframe data

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Contributed on Apr 25 2022

Astra Logical

0 Answers Avg Quality 2/10

Pandas DataFrame

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d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Popularity 10/10 Helpfulness 2/10 Language python

Source: pandas.pydata.org

Tags: dataframe data

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Contributed on Apr 08 2023

Timothy Ajewole

0 Answers Avg Quality 2/10

pandas DataFrame

Comment

df = pd.DataFrame(
   ....:     {
   ....:         "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
   ....:         "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
   ....:         "C": np.random.randn(8),
   ....:         "D": np.random.randn(8),
   ....:     }
   ....: )
   ....: 

In [88]: df
Out[88]: 
     A      B         C         D
0  foo    one  1.346061 -1.577585
1  bar    one  1.511763  0.396823
2  foo    two  1.627081 -0.105381
3  bar  three -0.990582 -0.532532
4  foo    two -0.441652  1.453749
5  bar    two  1.211526  1.208843
6  foo    one  0.268520 -0.080952
7  foo  three  0.024580 -0.264610

Popularity 10/10 Helpfulness 1/10 Language python

Source: pandas.pydata.org

Tags: dataframe data

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Contributed on Sep 16 2023

Muhammad Yousaf Rana

0 Answers Avg Quality 2/10

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