熊貓tv新功能介紹

Out of all technologies that is introduced in Data Analysis, Pandas is one of the most popular and widely used library.

在Data Analysis引入的所有技術(shù)中，P andas是最受歡迎和使用最廣泛的庫之一。

So what are we going to cover :

那么我們要講的是：

Installation of pandas

熊貓的安裝

Key components of pandas

大熊貓的主要成分

Read/Import data from CSV file

從CSV文件讀取/導(dǎo)入數(shù)據(jù)

Write/Export data to CSV files

將數(shù)據(jù)寫入/導(dǎo)出到CSV文件

Viewing and selecting data

查看和選擇數(shù)據(jù)

1.安裝熊貓 (1. Installation of pandas)

Let’s take care of the boring but important stuff first. Setting up the space to work with pandas.

首先讓我們處理無聊但重要的事情。 設(shè)置與熊貓共處的空間。

If you are using conda as your environment with miniconda or Anaconda then:

如果您使用的 暢達(dá) 與 miniconda 或 Python 那么 你的環(huán)境 ：

• Activate your environment
  激活您的環(huán)境

conda activate ./env

conda激活./env

• Install pandas package
  安裝熊貓包

conda install pandas

conda安裝熊貓

If you are using virtual environment with virtualenv then :

如果您通過virtualenv使用虛擬環(huán)境，則：

• Activate your environment
  激活您的環(huán)境

source ./env/bin/activate

源./env/bin/activate

• Install pandas package
  安裝熊貓包

pip install pandas

點安裝熊貓

If you are using virtual environment with pipenv then :

如果您通過pipenv使用虛擬環(huán)境，則：

• create and environment and install pandas in that environment
  在該環(huán)境中創(chuàng)建和環(huán)境并安裝熊貓

pipenv install pandas

pipenv安裝熊貓

• Activate the environment
  激活環(huán)境

pipenv shell

皮殼

2.大熊貓的主要成分 (2. Key components of pandas)

Pandas provides two compound data types, which are the key components of pandas that gives us so much flexibility on selecting, viewing and manipulating the data. Those two key components are:

熊貓?zhí)峁┝藘煞N復(fù)合數(shù)據(jù)類型，它們是熊貓的關(guān)鍵組成部分，這使我們在選擇，查看和操作數(shù)據(jù)方面具有如此大的靈活性。 這兩個關(guān)鍵組成部分是：

• Pandas Series
  熊貓系列
• Pandas Data Frame
  熊貓數(shù)據(jù)框

熊貓系列 (Pandas Series)

It is an one dimensional array offered by pandas. It can store different types of data ( meaning int,string, float, boolean etc..)

它是熊貓?zhí)峁┑囊痪S數(shù)組。 它可以存儲不同類型的數(shù)據(jù)(表示int，string，float，boolean等。)

A pandas series data be created as:

將熊貓系列數(shù)據(jù)創(chuàng)建為：

import pandas as pd

將熊貓作為pd導(dǎo)入

student_pass_percentage_in_country = pd.Series([“90”, “67”, “85”])

student_pass_percentage_in_country = pd.Series([“ 90”，“ 67”，“ 85”])

countries = pd.Series([“India”, “USA”, “China”])

國家= pd.Series([“印度”，“美國”，“中國”])

熊貓數(shù)據(jù)框 (Pandas Data Frame)

It is the one where most of the magic happens. It is a two dimensional array , you can think of it as an excel sheet.

這是大多數(shù)魔術(shù)發(fā)生的地方。 它是一個二維數(shù)組，您可以將其視為Excel工作表。

• The index in pandas starts from 0.
  熊貓的索引從0開始。
• The row is referred as axis=1 and column as axis=0.
  該行稱為axis = 1，而列稱為axis = 0。
• Its first column represents the index.
  它的第一列代表索引。
• More then one row can be associated with one index. So there are two ways of looking for data: one by index, one by position. Position also starts from 0.
  多于一行可以與一個索引相關(guān)聯(lián)。 因此，有兩種查找數(shù)據(jù)的方法：一種是按索引，一種是按位置。 位置也從0開始。

A pandas data frame can be created as:

熊貓數(shù)據(jù)框可以創(chuàng)建為：

student_pass_percent_by_country = pd.DataFrame({ ‘Country’: countries, ‘Pass Percent’: student_pass_percentage_in_country})

student_pass_percent_by_country = pd.DataFrame({'Country'：國家，'Pass Percent'：student_pass_percentage_in_country})

3.從CSV文件讀取/導(dǎo)入數(shù)據(jù) (3. Read / import data from CSV file)

First lets see how CSV file data looks like.

首先，讓我們看看CSV文件數(shù)據(jù)的外觀。

A CSV file contains data in comma separated format, which looks like:

CSV文件包含逗號分隔格式的數(shù)據(jù)，如下所示：

It looks like an excel sheet if you view on any excel viewer如果您在任何excel查看器上查看，它看起來像一個excel工作表 This is how it is in its raw format, when opened in any editor , in this i have opened in VS Coded在任何編輯器中打開時，它都是原始格式，在VS Coded中已打開

Reading CSV data is very straight forward in pandas. It provides you two functions : read_csv(‘file_path’) or read_csv(‘file_url’) , the data gets stored in data frame.

在熊貓中，讀取CSV數(shù)據(jù)非常簡單。 它提供了兩個功能：read_csv('file_path')或read_csv('file_url')，數(shù)據(jù)被存儲在數(shù)據(jù)框中。

i have taken this public repository from curran, so that you can use it as well.

我已經(jīng)從curran那里獲取了這個公共存儲庫，以便您也可以使用它。

csv_data = pd.read_csv(‘https://github.com/curran/data/blob/gh-pages/indiaGovOpenData/All_India_Index-February2016.csv’)

csv_data = pd.read_csv(' https://github.com/curran/data/blob/gh-pages/indiaGovOpenData/All_India_Index-February2016.csv ')

As you can see it right away tells us how many rows and columns are there in the data.

如您所見，它立即告訴我們數(shù)據(jù)中有多少行和多少列。

4.將數(shù)據(jù)寫入/導(dǎo)出到CSV文件 (4. Write/Export data to CSV files)

Exporting data to CSV file is as simple as importing it. Pandas has a function called : to_csv(‘file_name’), this will export the data from a data frame to CSV file.

將數(shù)據(jù)導(dǎo)出到CSV文件就像導(dǎo)入數(shù)據(jù)一樣簡單。 熊貓有一個名為：to_csv('file_name')的函數(shù)，它將數(shù)據(jù)從數(shù)據(jù)幀導(dǎo)出到CSV文件。

csv_data.to_csv(‘new_exported_data.csv;’)

csv_data.to_csv('new_exported_data.csv;')

5.查看和選擇數(shù)據(jù) (5. Viewing and Selecting data)

As we get to work with a lot of data so if we can view and select the data the way we want, it can give us more insights on the data at the first place.

當(dāng)我們開始處理大量數(shù)據(jù)時，如果我們可以按照自己的方式查看和選擇數(shù)據(jù)，那么它首先可以為我們提供關(guān)于數(shù)據(jù)的更多見解。

To view a snippet of data , ( 5 rows by default ):

要查看數(shù)據(jù)片段，(默認(rèn)為5行)：

csv_data.head()

csv_data.head()

To view more then just 5 records, let’s say you want to see 23 records from the top:

要查看僅5條記錄，假設(shè)您要從頂部查看23條記錄：

csv_data.head(23)

csv_data.head(23)

To view a snippet of data from bottom:

要從底部查看數(shù)據(jù)片段：

csv_data.tail()

csv_data.tail()

To view more then just 5 records from bottom, let’s say you want to see 11 records from the bottom:

要從底部僅查看5條記錄，假設(shè)您要從底部查看11條記錄：

csv_data.tail(11)

csv_data.tail(11)

To list out all the columns in the data:

列出數(shù)據(jù)中的所有列：

csv_data.columns

csv_data.columns

In pandas dataframe we can assign more then one data in an index. and the index starts from 0.

在pandas數(shù)據(jù)框中，我們可以在一個索引中分配多個數(shù)據(jù)。 索引從0開始。

sample_data = pd.DataFrame({‘name’: [‘Arun’, ‘Shiva’, ‘Rafah’], ‘a(chǎn)ge’: [12, 34, 45]}, index=[1, 1, 2])

sample_data = pd.DataFrame({'name'：['Arun'，'Shiva'，'Rafah']，'age'：[12，34，45]}，index = [1，1，2])

One thing you have noticed above is that , i can create data frame from plan python lists as well.

您在上面注意到的一件事是，我也可以從計劃python列表創(chuàng)建數(shù)據(jù)框。

View data at index 3:

查看索引3的數(shù)據(jù)：

sample_data.loc[1]

sample_data.loc [1]

View data at position 3:

查看位置3的數(shù)據(jù)：

sample_data.iloc[1]

sample_data.iloc [1]

Selecting a column , you can select a column in two ways

選擇列，您可以通過兩種方式選擇列

a. Dot notation:

一個。 點表示法：

sample_data.age

sample_data.age

b. Index/Attribute notation:

b。 索引/屬性符號：

sample_data[‘a(chǎn)ge’]

sample_data ['age']

The first option (a) will not work if the column name has spaces. So select one and stick to that.

如果列名包含空格，則第一個選項(a)將不起作用。 因此，選擇一個并堅持下去。

Selecting only those data where age is greater than 20:

僅選擇年齡大于20的那些數(shù)據(jù)：

sample_data[sample_data[‘a(chǎn)ge’] > 20]

sample_data [sample_data ['age']> 20]

I have just listed only most used functions here. I am planning to keep updating the article as i am going to refer it as well if i forget anything. If you have any questions or want to discuss any project feel free to comment here.

我在這里只列出了最常用的功能。 我打算繼續(xù)更新文章，因為如果我忘記了任何內(nèi)容，我也會參考它。 如果您有任何疑問或想要討論任何項目，請在此處發(fā)表評論。

Thank you for reading :)

謝謝您的閱讀:)

翻譯自: https://medium.com/@lax_17478/data-analysis-a-complete-introduction-to-pandas-part-1-3dd06922144a

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