用于数据分析的Python – Pandas

缺乏、安全感 2023-03-05 12:43 107阅读 0赞

## 大熊猫 **(**Pandas**)** ##

*  Pandas is an open-source library built on top of NumPy
    
    Pandas是建立在NumPy之上的开源库
 *  It allows for fast analysis and data cleaning and preparation
    
    它允许快速分析以及数据清理和准备
 *  It excels in performance and productivity
    
    它在性能和生产力方面都非常出色
 *  It also has built-in visualization features
    
    它还具有内置的可视化功能
 *  It can work with data from a wide variety of sources
    
    它可以处理来自各种来源的数据

### 如何安装熊猫？ **(**How to install Pandas?**)** ###

**Using PIP**

**使用画中画**

(venv) -bash-4.2$ pip install pandas
    Requirement already satisfied: pandas in ./venv/lib/python3.6/site-packages (0.25.1)
    Requirement already satisfied: python-dateutil>=2.6.1 in ./venv/lib/python3.6/site-packages (from pandas) (2.8.0)
    Requirement already satisfied: pytz>=2017.2 in ./venv/lib/python3.6/site-packages (from pandas) (2019.2)
    Requirement already satisfied: numpy>=1.13.3 in ./venv/lib/python3.6/site-packages (from pandas) (1.17.2)
    Requirement already satisfied: six>=1.5 in ./venv/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)
    venv) -bash-4.2$

**Series**

**系列**

**One-dimensional ndarray with axis labels, including time series**. It is capable of holding data of any type. The axis labels are collectively known as an index. Series is very similar to a NumPy array, built on NumPy array object. However, the difference being a series can be indexed by labels.

**具有轴标签的一维ndarray，包括时间序列** 。 它能够保存任何类型的数据。 轴标签统称为索引。 系列与建立在NumPy数组对象上的NumPy数组非常相似。 但是，区别在于可以通过标签对系列进行索引。

**Syntax:**

**句法：**

class pandas.Series(
        data=None, 
        index=None, dtype=None, 
        name=None, 
        copy=False, 
        fastpath=False
        )

Below snippets shows examples of creating a series,

以下代码片段显示了创建系列的示例，

import numpy as np
    import pandas as pd
    
    labels = ['a','e','i','o'] #python list
    data = [1,2,3,4] #python list
    arr = np.array(data) #NumPy array
    d = {
        'a':1,'b':2,'c':3} #python dict
    
    # creating a series object with default index
    print(pd.Series(data = data))
    
    # creating a series object with labels as index
    print(pd.Series(data = data, index = labels))
    
    # creating a series with NumPy array
    print(pd.Series(arr,index = labels))
    
    # creating a series with dictionary, 
    # here the key becomes the index
    print(pd.Series(d))
    
    # Series can also hold built-in func
    print(pd.Series(data = [sum, print, len]))

**Output**

**输出量**

0    1
    1    2
    2    3
    3    4
    dtype: int64
    a    1
    e    2
    i    3
    o    4
    dtype: int64
    a    1
    e    2
    i    3
    o    4
    dtype: int64
    a    1
    b    2
    c    3
    dtype: int64
    0       <built-in function sum>
    1       <built-in function print>
    2       <built-in function len>
    dtype: object

### 系列操作 **(**Operations on Series**)** ###

**Create two series object**

**创建两个系列对象**

import pandas as pd
    
    ser1 = pd.Series([1,2,3,4],['Delhi','Bangalore','Mysore', 'Pune'])
    print(ser1)
    
    ser2 = pd.Series([1,2,5,4],['Delhi','Bangalore','Vizag','Pune'])
    print(ser2)

**Output**

**输出量**

Delhi        1
    Bangalore    2
    Mysore       3
    Pune         4
    dtype: int64
    Delhi        1
    Bangalore    2
    Vizag        5
    Pune         4
    dtype: int64

To retrieve the information from the series, is similar to the python dictionary, pass on the index-label of the given data type. In the above example, the index-label is of type String.

要从系列中检索信息，类似于python字典，传递给定数据类型的index-label。 在上面的示例中，索引标签的类型为String。

print(ser1['Delhi'])
    # Output: 1

Now let's trying adding the two series,

现在让我们尝试添加两个系列，

print(ser1+ser2)
    '''
    Output:
    Bangalore    4.0
    Delhi        2.0
    Mysore       NaN
    Pune         8.0
    Vizag        NaN
    dtype: float64
    '''

The **pandas**, adds the values of the index-labels. In case the match is not found, it will be put a NaN (null value). When the operations are performed on series or any **NumPy/Pandas** based object, the integers will be converted to float.

**pandas** ，添加索引标签的值。 如果找不到匹配项，则将其放入NaN(空值)。 当对序列或任何基于**NumPy / Pandas**的对象执行操作时，整数将转换为float。

> 翻译自: [https://www.includehelp.com/python/python-for-data-analysis-pandas.aspx][https_www.includehelp.com_python_python-for-data-analysis-pandas.aspx]

[https_www.includehelp.com_python_python-for-data-analysis-pandas.aspx]: https://www.includehelp.com/python/python-for-data-analysis-pandas.aspx