pandas basics

pandas is a data analysis package built based on Numpy that contains more advanced data structures and tools

Similar to Numpy, whose core is ndarray, pandas also revolves around the two core data structures of Series and DataFrame. Series and DataFrame correspond to one-dimensional sequence and two-dimensional table structure respectively. The conventional import method of pandas is as follows:

from pandas import Series,DataFrame
import pandas as pd

Series

---

Series can be regarded as a fixed-length ordered dictionary. Basically any one-dimensional data can be used to construct Series objects:

>>> s = Series([1,2,3.0,'abc'])
>>> s
0      1
1      2
2      3
3    abc
dtype: object

Although dtype:object can contain a variety of basic data types, it always feels like it will affect performance. It is best Or keep it simple dtype.

The Series object contains two main attributes: index and values, which are the left and right columns in the above example. Because what is passed to the constructor is a list, the value of index is an integer that increases from 0. If a dictionary-like key-value pair structure is passed in, a Series corresponding to index-value will be generated; or in the initialization When using keyword parameters to explicitly specify an index object:

>>> s = Series(data=[1,3,5,7],index = ['a','b','x','y'])
>>> s
a    1
b    3
x    5
y    7
dtype: int64
>>> s.index
Index(['a', 'b', 'x', 'y'], dtype='object')
>>> s.values
array([1, 3, 5, 7], dtype=int64)

The elements of the Series object will be constructed strictly according to the given index, which means: if the data parameter has a key-value pair, then only the elements in the index The key contained will be used; and if the corresponding key is missing from data, the key will be added even if a NaN value is given.

Note that although there is a correspondence between the index of Series and the elements of values, this is different from the mapping of dictionary. Index and values ​​are actually still independent ndarray arrays, so the performance of Series objects is completely ok.

Series The biggest advantage of this data structure using key-value pairs is that the index will be automatically aligned when arithmetic operations are performed between Series.

In addition, the Series object and its index both contain a name attribute:

>>> s.name = 'a_series'
>>> s.index.name = 'the_index'
>>> s
the_index
a            1
b            3
x            5
y            7
Name: a_series, dtype: int64

DataFrame

---

DataFrame It is a tabular data structure that contains a set of ordered columns (similar to index), and each column can be of a different value type (unlike ndarray, which can only have one dtype). Basically, you can think of a DataFrame as a collection of Series that share the same index.

The construction method of DataFrame is similar to Series, except that it can accept multiple one-dimensional data sources at the same time, and each one will become a separate column:

>>> data = {'state':['Ohino','Ohino','Ohino','Nevada','Nevada'],
        'year':[2000,2001,2002,2001,2002],
        'pop':[1.5,1.7,3.6,2.4,2.9]}
>>> df = DataFrame(data)
>>> df
   pop   state  year
0  1.5   Ohino  2000
1  1.7   Ohino  2001
2  3.6   Ohino  2002
3  2.4  Nevada  2001
4  2.9  Nevada  2002

[5 rows x 3 columns]

Although the parameter data looks like a dictionary, The keys of the dictionary do not play the role of the index of the DataFrame, but the "name" attribute of the Series. The index generated here is still "01234".

The more complete DataFrame constructor parameters are: DataFrame(data=None,index=None,coloumns=None), columns is "name":

>>> df = DataFrame(data,index=['one','two','three','four','five'],
               columns=['year','state','pop','debt'])
>>> df
       year   state  pop debt
one    2000   Ohino  1.5  NaN
two    2001   Ohino  1.7  NaN
three  2002   Ohino  3.6  NaN
four   2001  Nevada  2.4  NaN
five   2002  Nevada  2.9  NaN

[5 rows x 4 columns]

Similarly Missing values ​​are filled with NaN. Take a look at index, columns and index types:

>>> df.index
Index(['one', 'two', 'three', 'four', 'five'], dtype='object')
>>> df.columns
Index(['year', 'state', 'pop', 'debt'], dtype='object')
>>> type(df['debt'])
<class &#39;pandas.core.series.Series&#39;>

DataFrame row-oriented and column-oriented operations are basically balanced, and any column extracted is a Series.

Object properties

---

Reindex

Series objects are reindexed through their .reindex(index=None,**kwargs) method accomplish. There are two commonly used parameters in **kwargs: method=None,fill_value=np.NaN:

ser = Series([4.5,7.2,-5.3,3.6],index=['d','b','a','c'])
>>> a = ['a','b','c','d','e']
>>> ser.reindex(a)
a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64
>>> ser.reindex(a,fill_value=0)
a   -5.3
b    7.2
c    3.6
d    4.5
e    0.0
dtype: float64
>>> ser.reindex(a,method='ffill')
a   -5.3
b    7.2
c    3.6
d    4.5
e    4.5
dtype: float64
>>> ser.reindex(a,fill_value=0,method='ffill')
a   -5.3
b    7.2
c    3.6
d    4.5
e    4.5
dtype: float64

.reindex() method A new object will be returned, its index strictly follows the given parameters, method:{'backfill', 'bfill', 'pad', 'ffill', None} Parameters are used to specify interpolation (filling) Method, when not given, automatically fills with fill_value, the default is NaN (ffill = pad, bfill = back fill, respectively refers to the forward or backward value during interpolation)

The reindexing method of the DataFrame object is: .reindex(index=None,columns=None,**kwargs). There is only one more optional columns parameter than Series, which is used to index the columns. The usage is similar to the above example, except that the interpolation method method parameter can only be applied to rows, that is, axis 0.

>>> state = ['Texas','Utha','California']
>>> df.reindex(columns=state,method='ffill')
    Texas  Utha  California
a      1   NaN           2
c      4   NaN           5  
d      7   NaN           8

[3 rows x 3 columns]
>>> df.reindex(index=['a','b','c','d'],columns=state,method='ffill')
   Texas  Utha  California
a      1   NaN           2
b      1   NaN           2
c      4   NaN           5
d      7   NaN           8

[4 rows x 3 columns]

But fill_value is still valid. Smart friends may have already thought about it, can we implement interpolation on columns through df.T.reindex(index,method='**').T? The answer is yes. of. Also note that when using reindex(index,method='**'), index must be monotonic, otherwise it will trigger a ValueError: Must be monotonic for forward fill , for example, the last call in the above example will not work if index=['a','b','d','c'] is used.

Deleting items on the specified axis

means deleting an element of the Series or a certain row (column) of the DataFrame, through the object's .drop(labels, axis=0) Method:

>>> ser
d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64
>>> df
   Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8

[3 rows x 3 columns]
>>> ser.drop('c')
d    4.5
b    7.2
a   -5.3
dtype: float64
>>> df.drop('a')
   Ohio  Texas  California
c     3      4           5
d     6      7           8

[2 rows x 3 columns]
>>> df.drop(['Ohio','Texas'],axis=1)
   California
a           2
c           5
d           8

[3 rows x 1 columns]

.drop() Returns a new object, and the meta object will not be changed.

Indexing and slicing

Like Numpy, pandas also supports indexing and slicing through obj[::], as well as filtering through boolean arrays.

However, it should be noted that because the index of the pandas object is not limited to integers, when using a non-integer as the slice index, it is included at the end.

>>> foo
a    4.5
b    7.2
c   -5.3
d    3.6
dtype: float64
>>> bar
0    4.5
1    7.2
2   -5.3
3    3.6
dtype: float64
>>> foo[:2]
a    4.5
b    7.2
dtype: float64
>>> bar[:2]
0    4.5
1    7.2
dtype: float64
>>> foo[:'c']
a    4.5
b    7.2
c   -5.3
dtype: float64

这里 foo 和 bar 只有 index 不同——bar 的 index 是整数序列。可见当使用整数索引切片时，结果与 Python 列表或 Numpy 的默认状况相同；换成 'c' 这样的字符串索引时，结果就包含了这个边界元素。

另外一个特别之处在于 DataFrame 对象的索引方式，因为他有两个轴向（双重索引）。

可以这么理解：DataFrame 对象的标准切片语法为：.ix[::,::]。ix 对象可以接受两套切片，分别为行（axis=0）和列（axis=1）的方向：

>>> df
   Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8

[3 rows x 3 columns]
>>> df.ix[:2,:2]
   Ohio  Texas
a     0      1
c     3      4

[2 rows x 2 columns]
>>> df.ix['a','Ohio']
0

而不使用 ix ，直接切的情况就特殊了：

• 索引时，选取的是列

• 切片时，选取的是行

这看起来有点不合逻辑，但作者解释说 “这种语法设定来源于实践”，我们信他。

>>> df['Ohio']
a    0
c    3
d    6
Name: Ohio, dtype: int32
>>> df[:'c']
   Ohio  Texas  California
a     0      1           2
c     3      4           5

[2 rows x 3 columns]
>>> df[:2]
   Ohio  Texas  California
a     0      1           2
c     3      4           5

[2 rows x 3 columns]

使用布尔型数组的情况，注意行与列的不同切法（列切法的 : 不能省）：

>>> df['Texas']>=4
a    False
c     True
d     True
Name: Texas, dtype: bool
>>> df[df['Texas']>=4]
   Ohio  Texas  California
c     3      4           5
d     6      7           8

[2 rows x 3 columns]
>>> df.ix[:,df.ix['c']>=4]
   Texas  California
a      1           2
c      4           5
d      7           8

[3 rows x 2 columns]

算术运算和数据对齐

pandas 最重要的一个功能是，它可以对不同索引的对象进行算术运算。在将对象相加时，结果的索引取索引对的并集。自动的数据对齐在不重叠的索引处引入空值，默认为 NaN。

>>> foo = Series({'a':1,'b':2})
>>> foo
a    1
b    2
dtype: int64
>>> bar = Series({'b':3,'d':4})
>>> bar
b    3
d    4
dtype: int64
>>> foo + bar
a   NaN
b     5
d   NaN
dtype: float64

DataFrame 的对齐操作会同时发生在行和列上。

当不希望在运算结果中出现 NA 值时，可以使用前面 reindex 中提到过 fill_value 参数，不过为了传递这个参数，就需要使用对象的方法，而不是操作符：df1.add(df2,fill_value=0)。其他算术方法还有：sub(), div(), mul()。

Series 和 DataFrame 之间的算术运算涉及广播，暂时先不讲。

函数应用和映射

Numpy 的 ufuncs（元素级数组方法）也可用于操作 pandas 对象。

当希望将函数应用到 DataFrame 对象的某一行或列时，可以使用 .apply(func, axis=0, args=(), **kwds) 方法。

f = lambda x:x.max()-x.min()
>>> df
   Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8

[3 rows x 3 columns]
>>> df.apply(f)
Ohio          6
Texas         6
California    6
dtype: int64
>>> df.apply(f,axis=1)
a    2
c    2
d    2
dtype: int64

排序和排名

Series 的 sort_index(ascending=True) 方法可以对 index 进行排序操作，ascending 参数用于控制升序或降序，默认为升序。

若要按值对 Series 进行排序，当使用 .order() 方法，任何缺失值默认都会被放到 Series 的末尾。

在 DataFrame 上，.sort_index(axis=0, by=None, ascending=True) 方法多了一个轴向的选择参数与一个 by 参数，by 参数的作用是针对某一（些）列进行排序（不能对行使用 by 参数）：

>>> df.sort_index(by='Ohio')
   Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8

[3 rows x 3 columns]
>>> df.sort_index(by=['California','Texas'])
   Ohio  Texas  California
a     0      1           2
c     3      4           5
d     6      7           8

[3 rows x 3 columns]
>>> df.sort_index(axis=1)
   California  Ohio  Texas
a           2     0      1
c           5     3      4
d           8     6      7

[3 rows x 3 columns]

排名（Series.rank(method='average', ascending=True)）的作用与排序的不同之处在于，他会把对象的 values 替换成名次（从 1 到 n）。这时唯一的问题在于如何处理平级项，方法里的 method 参数就是起这个作用的，他有四个值可选：average, min, max, first。

>>> ser=Series([3,2,0,3],index=list('abcd'))
>>> ser
a    3
b    2
c    0
d    3
dtype: int64
>>> ser.rank()
a    3.5
b    2.0
c    1.0
d    3.5
dtype: float64
>>> ser.rank(method='min')
a    3
b    2
c    1
d    3
dtype: float64
>>> ser.rank(method='max')
a    4
b    2
c    1
d    4
dtype: float64
>>> ser.rank(method='first')
a    3
b    2
c    1
d    4
dtype: float64

注意在 ser[0]=ser[3] 这对平级项上，不同 method 参数表现出的不同名次。

DataFrame 的 .rank(axis=0, method='average', ascending=True) 方法多了个 axis 参数，可选择按行或列分别进行排名，暂时好像没有针对全部元素的排名方法。

统计方法

pandas 对象有一些统计方法。它们大部分都属于约简和汇总统计，用于从 Series 中提取单个值，或从 DataFrame 的行或列中提取一个 Series。

比如 DataFrame.mean(axis=0,skipna=True) 方法，当数据集中存在 NA 值时，这些值会被简单跳过，除非整个切片（行或列）全是 NA，如果不想这样，则可以通过 skipna=False 来禁用此功能：

>>> df
    one  two
a  1.40  NaN
b  7.10 -4.5
c   NaN  NaN
d  0.75 -1.3

[4 rows x 2 columns]
>>> df.mean()
one    3.083333
two   -2.900000
dtype: float64
>>> df.mean(axis=1)
a    1.400
b    1.300
c      NaN
d   -0.275
dtype: float64
>>> df.mean(axis=1,skipna=False)
a      NaN
b    1.300
c      NaN
d   -0.275
dtype: float64

其他常用的统计方法有： 

##******** ************************************countNumber of non-NA valuesdescribeCompute summary statistics for columns of Series or DF##argmin, argmaxIndex position of the minimum and maximum values ​​(integer)idxmin, idxmaxIndex values ​​of the minimum and maximum valuesquantileSample quantile (0 to 1 )sumSummeanmeanmedianMedianmadCalculate the average absolute dispersion based on the meanvarVariancestdStandard Deviationskew Skewness of sample values ​​(third moment)kurtKurtosis of sample values ​​(fourth moment)cumsumCumulative sum of sample valuescummin, cummaxCumulative maximum value and cumulative minimum value of sample valuescumprodCumulative product of sample valuesdiffCalculate the first difference (useful for time series)pct_change

##min , max	Minimum and maximum values

Calculate percent change

---

##Handle missing data

The main expression of NA in pandas is np.nan. In addition, Python's built-in None will also be treated as NA.

There are four ways to handle NA:

dropna, fillna, isnull, notnull

.

is(not)null

This pair of methods performs element-level applications on the object, and then returns a Boolean array, which can generally be used for Boolean indexing.

dropnaFor a Series, dropna returns a Series containing only non-null data and index values.

The problem lies in the way DataFrame is processed, because once it is dropped, at least one row (column) must be lost. The solution here is similar to the previous one, but it still passes an additional parameter:

dropna(axis=0, how='any', thresh=None)

. The optional value of the how parameter is any or all. all discards the row (column) only if all slice elements are NA. Another interesting parameter is thresh, which is of type integer. Its function is that, for example, thresh=3, it will be retained when there are at least 3 non-NA values ​​in a row. fillna

fillna(value=None, method=None, axis=0)

In addition to the basic type, the value parameter in can also use a dictionary, so that it can be achieved Fill different columns with different values. The usage of method is the same as the previous

.reindex()

---

method, so I won’t go into details here.

inplace parameter

###There is a point that I haven’t mentioned before, but after writing the entire example, I found that it is quite important. Among the methods of Series and DataFrame objects, any method that modifies the array and returns a new array often has an optional parameter of ###replace=False###. If manually set to True, the original array can be replaced. ###

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