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[bfillsmarketing]

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[dallasnovelty]

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[punxsutawneyfilth]

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[sahoooda]

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bswish Bfilled Basic Slate Prostate Massager

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[travelindirty]

2/5/2019 - "The Medicine of Modernity: A True Story"- Hornbrook, California

I wish I were lying. I wish it weren't true, but where once a mighty river connected with a fertile creek, lived a people we may never meet.

They had their traditions, and a complete culture. They had a medicine man, who took care of his people. They had a land and a way of living, on top of gold on the banks of salmon filled waters.

But now, on that spot, we have a bar, filled with smoke, and video poker, with the hides of bears and the heads of elk decorating the wall. Here the staff of that medicine man hangs, on a mirror carved with naked ladies, above a glass shelf of liquor bottles and Betty Boop memorbilia. It was given to her father, she said. She doesn't know much about it. Our modern medicine woman pours a thick shot into heavy bottomed shot glass. Thanks, ma'am. This too will go down in history. If we can remember.

[linux-ubuntu-it]

Microsoft Windows 10 x64 RS2 win32kfull!bFill Overflow

This is a collection of exploits for the recently-patched win32kfull!bFill vulnerability. Executing the Palette or Bitmap exploit will give you SYSTEM privileges on the affected system. The exploits should work fine on Windows 10 x64 with Creators Update, build 15063.540 (latest version of Win10 before the release of Microsoft’s September Updates). Source: Microsoft Windows 10 x64 RS2…

[shilkaren]

Reindexing in Python Pandas

Reindexing is used to change the row labels and column labels of a DataFrame.

It means to conform the data to match a given set of labels along a particular axis.

It helps us to perform Multiple operations through indexing like –

To insert missing value (NaN) markers in label locations where no              data for the label existed before.

To reorder the existing data to match a new set of labels.

 Example

import pandas as pd

import numpy as np

N=20

data = pd.DataFrame({

  'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),

  'x': np.linspace(0,stop=N-1,num=N),

  'y': np.random.rand(N),

  'C': np.random.choice(['Low','Medium','High'],N).tolist(),

  'D': np.random.normal(100, 10, size=(N)).tolist()

})

#reindexing the DataFrame

data_reindexed = data.reindex(index=[0,2,5], columns=['A', 'C', 'B'])

print(data_reindexed)

 Output:

          A     C   B

0 2016-01-01  High NaN

2 2016-01-03   Low NaN

5 2016-01-06  High NaN

 How to Reindex to Align with Other Objects?

Lets us consider if you want to take an object and reindex its axes and labeled the same as another object.

Take an example to get better understanding

 Example:

import pandas as pd

import numpy as np

data1 = pd.DataFrame(np.random.randn(10,3),columns=['column1','column2','column3'])

data2 = pd.DataFrame(np.random.randn(7,3),columns=['column1','column2','column3'])

data1 = data1.reindex_like(data2)

print(data1)

Output

   column1   column2   column3

0 0.271240  0.201199 -0.151743

1 -0.269379  0.262300 0.019942

2 0.685737 -0.233194 -0.652832

3 -1.416394 -0.587026  1.065789

4 -0.590154 -2.194137  0.707365

5 0.393549  1.801881 -2.529611

6 0.062660 -0.996452 -0.029740

Note − Here, the data1 DataFrame is altered and reindexed like data2. If the column names do not should be matched NaN will be added for the entire column label.

How to Fill values while ReIndexing?

We can also fill the missing value while we are reindexing the dataset.

Pandas reindex() method takes an optional parameter which helps to fill the values. The parameters are as follows-

·        pad/ffill – It will fill values in forward direction.

·        bfill/backfill – It will fill the values backward direction.

·        nearest – It will fill the values from the nearest index values.

 Example

import pandas as pd

import numpy as np

df1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])

df2 = pd.DataFrame(np.random.randn(2,3),columns=['col1','col2','col3'])

# Padding NAN's

print(df2.reindex_like(df1))

# Now Fill the NAN's with preceding Values

print ("Data Frame with Forward Fill:")

print (df2.reindex_like(df1,method='ffill'))

Output

      col1      col2      col3

0 -1.046918  0.608691 1.081329

1 -0.396384 -0.176895 -1.896393

2       NaN       NaN       NaN

3       NaN       NaN       NaN

4       NaN       NaN       NaN

5       NaN       NaN       NaN

Data Frame with Forward Fill:

      col1      col2      col3

0 -1.046918  0.608691 1.081329

1 -0.396384 -0.176895 -1.896393

2 -0.396384 -0.176895 -1.896393

3 -0.396384 -0.176895 -1.896393

4 -0.396384 -0.176895 -1.896393

5 -0.396384 -0.176895 -1.896393

Note – In the above example the last four rows are padded.

 How to Limit on Filling values while Reindexing?

Reindex() function also takes a parameter “limit” which is used to maximum count of the consecutive matches.

Let’s understand with an example-

Example

import pandas as pd

import numpy as np

df1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])

df2 = pd.DataFrame(np.random.randn(2,3),columns=['col1','col2','col3'])

# Padding NAN's

print(df2.reindex_like(df1))

# Now Fill the NAN's with preceding Values

print ("Data Frame with Forward Fill limiting to 1:")

print(df2.reindex_like(df1,method='ffill',limit=1))

 Output

      col1      col2     col3

0 0.824697  0.122557 -0.156242

1 0.528174 -1.140847 -1.158778

2       NaN       NaN       NaN

3       NaN       NaN       NaN

4       NaN       NaN       NaN

5       NaN       NaN       NaN

Data Frame with Forward Fill limiting to 1:

      col1      col2      col3

0 0.824697  0.122557 -0.156242

1 0.528174 -1.140847 -1.158778

2 0.528174 -1.140847 -1.158778

3       NaN       NaN       NaN

4       NaN       NaN       NaN

5       NaN       NaN       NaN

Note – In the above we can observe that only the 7th row is filled by the preceding 6th row. Then, the rows are left as they are.

How to Rename in Python?

Python provides a rename() method which allows us to relabel an axis based on the same mapping (a dict or a Series) or an arbitrary function.

Let’s take an example to understand

Example

import pandas as pd

import numpy as np

data1 = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])

print(data1)

print ("After renaming the rows and columns:")

print(data1.rename(columns={'col1' : 'c1', 'col2' : 'c2'},

index = {0 : 'apple', 1 : 'banana', 2 : 'mango'}))

Output

      col1      col2      col3

0 0.047170  0.378306 -1.198150

1 1.183208 -2.195630 -0.798192

2 0.256581  0.627994 -0.674260

3 0.240853  1.677340  1.497613

4 0.820688  0.920151 -1.431485

5 -0.010474 -0.228373 -0.392640

After renaming the rows and columns:

             c1        c2     col3

apple   0.047170  0.378306 -1.198150

banana 1.183208 -2.195630 -0.798192

mango   0.256581  0.627994 -0.674260

3       0.240853  1.677340  1.497613

4       0.820688  0.920151 -1.431485

5     -0.010474 -0.228373 -0.392640

This rename() method provides an inplace named parameter, which by default is False and copies the underlying data. Pass inplace=True to rename the data in place.

Insideaiml is one of the best platforms where you can learn Python, Data Science, Machine Learning, Artificial Intelligence & showcase your knowledge to the outside world.

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It may look small and discreet, but this prostate-stimulating plug expertly reaches the p-spot to deliver stronger orgasms without any additional stimulation, and without a motor. The Bfilled Basic is carefully designed with a tapered head and narrow neck for maximum wearable comfort, and a properly flared base for secure play. Crafted from 100% body-safe silicone, the silky-smooth texture will…

[andorsex]

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