Point to open to see some, you will love learning pandas, so you should learn! No.3

As a long-winded blogger, or to say Diansha, if you do not want to learn, drag and drop directly to the final, he found eggs, then 3 seconds

I do, there is a small goal, is to put technology like the article, write interesting

Programming it, what is the hardest?

I voted for the entry

In fact, a person programmed into the door is the hardest

And the dream eraser chiefs, in doing this
(it plainly, is to write the article profound, but also pretended chiefs, tired heart, hard to find such an excuse, really happy)

I want to pass a series of articles pandas

This allows you to learn a simple module

Then also the way to write fun stuff

Miya ~

Each article, let you read it silky smooth

Go ahead and pandas, series function

Part blog, we will go a little understanding of the function of a series of Diudiu

Enough
of this, let's continue

Heart meditation

pandas is processing data, the processing is the number of digits

OK, GET to this was much better

Later I made up just fine

import pandas as pd
s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])
print(s)

I created a basic Series, then it should be dealt with

For a linear data for

We can do many things

For example, I want to get the maximum and minimum

import pandas as pd

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])

print(s.min())
print(s.max())

This wording is too simple, it does not show up we learn something

We get some fresh (actually the official website of the more complex examples)

Give a series to create an index with the level of

With the level of the index, What do you mean?

In fact, excel merged cells inside
look at the code

idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm', 'cold', 'cold'],
    ['dog', 'falcon', 'fish', 'spider']]
    ,names=['blooded', 'animal'])

s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
print(s)

A cold-blooded and warm-blooded animals on the table regarding how many feet

Well, a long piece of text output long like this

Do not understand, it does not matter, put excel inside Chou Chou

What kind of loud noise, a small version of a row, clarity

On a table
ahead of the current index is still
real data on the row behind

The operator then some

idx = pd.MultiIndex.from_arrays([
    ['warm', 'warm','warm',  'cold', 'cold'],
    ['dog', 'falcon','people', 'fish', 'spider']]
    ,names=['blooded', 'animal'])

s = pd.Series([4, 2,2, 0, 8], name='legs', index=idx)
print(s)
print(s.min())
print(s.max(level='blooded'))

The latter is to control the level of multiple indexes oh ~

See the results, you can understand it in seconds?

s = pd.Series([4, 2,2, 0, 8], name='legs', index=idx)
print(s)
print(s.min())  # 输出0
print(s.max(level='blooded')) # 输出 下面的表格

Yes, I am following table

Learn it, the learned, min, max, sum, idxmin, idxmax you will be a

What idx is what? Take a look at the above code
that is indexed ... ...

But also a long-winded property

For the series, the property also has a very, very important, after important to use, lacks effect?

This property is T
Yes, a capital letter T

Ha ha ha, in fact, this attribute for series, the basic futile

After using or equal to itself

s = pd.Series([3,1,4],index=['a','b','c'])
print(s)
print(s.T)

Continue to look at the function

Like wandering, and how to see the property

Continue to look at the function ah

A linear messy data, for us,
can also sort ah

import pandas as pd

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])

Since the sort, as you guess, you can guess (in fact, most people simply could not guess, the teacher will think you can guess)

A is ordered by values, in accordance with a sorting index

import pandas as pd

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])

print(s.sort_values())
print(s.sort_index())

Some sort of parameters, can often

Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')

• axis which reference axis sorted for Series, it can only be set to 0
• ascending descending or ascending positive sequence True
• inplace default false, amended as true, in situ modification? Hey, I do not understand it, for a while I'll give you an chestnuts
• kind sorting method quick sort, merge sort, heap sort
• na_position null value, or at the front post, this, you try to know the

inplace

Look at the following code, replace situ

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])
sorted_s = s.sort_values(inplace=True)
print(sorted_s)

This time print out is None, but you can print s, text can appear after ordering

I did not understand?
S is the variable directly to the sort of

After sorting it out, we should attempt to get part of series

Get a few head

Head head head

Acquired at the end of a few

tail,tail

import pandas as pd

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])

print(s.head(2))
print(s.tail(2))

Data acquisition section can, then certainly you can delete the data myself
(I unhesitatingly say)

Series.drop

import pandas as pd

s = pd.Series([3,1,4,1,5,9,2,6,8,3,6])

print(s.drop(labels=[0,1]))

For the series is, labels parameter is mandatory

why? Since the other simply does not support

The new version can be replaced with index labels

Well, they learn new knowledge of it

Not too much, just a few

Daily learn a little of it, ah

Diansha write tomorrow, it may be DataFrame

Finally, welcome attention to the public a number of nagging programming engineers, undergraduate non-programmers

Took out your cell phone, take this