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one。 Introduction to Series

Series is an object similar to an one-dimensional array, which consists of a set of data (various NumPy data types) and a set of data tags associated with it (that is, indexes). A simple Series object can be generated from only one set of data.

Series is basically a tagged one-dimensional array that can store integers, floating-point numbers, strings, Python objects, and so on. The label axis is often called an index.

two。 Instantiate Series2.1 instantiate using an one-dimensional array

When instantiating Series with an one-dimensional array, the index length must be the same as the array length. When no index is specified, Pandas will help us create a default numeric index.

In [1]: S1 = pd.Series ([1, 2, 3, 4]) Out [1]: 0 11 22 33 4dtype: int64In [2]: S2 = pd.Series ([1, 2, 3, 4], index= ['await,' baked, 'cased,' d']) Out [2]: a 1b 2c 3D 4dtype: int64

Note: Pandas supports duplicate indexes. However, we can also reset the index, which will be given in subsequent chapters.

2.2 instantiation using a dictionary

When instantiating Series using a dictionary, if no index is passed in, the value of the index is the key of the dictionary:

In [1]: pd.Series ({'itrees: 0,' jacks: 1, 'kink: 2}) Out [1]: I 0j 1k 2dtype: int642.3 using scalar instantiation

When instantiating with scalar values, an index must be provided. Series repeats the scalar value by index length.

In [1]: pd.Series (6, index= [0,1,2]) Out [1]: 0662 6dtype: int64 III. Series simply uses 3.1 to add Name attributes to Series

When instantiating Series, you can pass in the name parameter to add the name attribute for Series. Seires also supports renaming:

In [1]: s = pd.Series (6, index= [0,1,2], name='six') Out [1]: 061 62 6Name: six, dtype: int64In [2]: s.nameOut [2]: 'six'In [3]: s = s.rename (' sixsixsix') In [4]: s.nameOut [4]: 'sixsixsix'3.2 location-based slicing

Series provides slicing methods similar to Python lists:

In [0]: s = pd.Series ([1,2,3,4], index= ['await,' baked, 'cased,' d']) In [1]: s [0:2] # take two data labeled 0 and 1 (excluding 2, that is, starting with the first one) Take two data) Out [1]: a 1b 2dtype: int64In [2]: s [: 3] # take the first three data Out [2]: a 1b 2c 3dtype: int64In [3]: s [- 2:] # take the last two data (which can also be understood as taking the second data all the way back to the end) Out [3]: C 3D 4dtype: int64In [4]: s 3]] # take the data 1, 3 and 4 (note that the subscript starts with 0 Conversion to location requires + 1) Out [4]: a 1c 3D 4dtype: int64 # Note: if the input location is greater than the length of the list, an "indexers are out-of-bounds" exception will be reported 3.3.Index-based slices

Series can use the value of the index tag to extract the value:

In [0]: s = pd.Series ([1, 2, 3, 4], index= ['a', 'baked,' c','d']) In [1]: s ['a'] # extract s, the value Out [1]: a 1dtype: int64In [1]: s [['a', 'baked,' c']] # extract s Values labeled a, b, c Out [1]: a 1b 2c 3dtype: int64

If the value of the passed index tag is not in the axis index of Seires, a KeyError exception will be reported. It is recommended that you use the get method of Series to obtain the value. If it does not exist, None will be returned. You can also set the default parameter for the default return value if it does not exist.

In [0]: s = pd.Series ([1,2,3,4], index= ['await,' baked, 'cased,' d']) In [1]: s ['f'] # extract s, the value of the label f, if f does not exist, an exception Out will be reported [1]: KeyErrorIn [2]: s.get ('f') # extract s, the value of the label f, if f does not exist Default returns NoneOut [2]: NoneIn [3]: s.get ('fallow. Default=-1) # extract s with the label f. If f does not exist, return-1Out [3]:-13.4 condition-based slices In [0]: s = pd.Series ([1,2,3,4], index= ['aural,' baked, 'cased,' d']) In [1]: s

S.mean ()] # in extraction s, values greater than the average Out [1]: C 3D 4dtype: int64In [1]: s [s.between (1jue 3, inclusive=False)] # extraction s, data between 1 2dtype 3 (excluding 1Magne3) Out [1]: B 2dtype: int64

When extracting interval data, if you want the values at both ends to be included (satisfying that the values at both ends are also extracted), you only need to assign the value of the inclusive parameter to True

3.5 other actions

Series can perform mathematical operations as quickly as a single numeric value without looping:

In [0]: s = pd.Series ([1,2,3,4], index= ['await,' baked, 'cased,' d']) In [1]: s + sOut [1]: a 2b 4c 6d 8dtype: int64In [2]: s-1Out [2]: a 0b 1c 2d 3dtype: int64

Operations between Series automatically align data based on tags. If a tag in one Series does not exist in another Series, the result of the calculation will be NaN, the missing value, and the treatment of the missing value NaN will be discussed in later chapters. Therefore, we don't have to worry about whether the Series performing the operation has the same tag. The function of data alignment integrated by Pandas data structure is an important feature that distinguishes Pandas from most tag-based data processing tools.

In [0]: S1 = pd.Series ([1,2,3,4], index= ['await,' baked, 'cased,' d']) In [0]: S2 = pd.Series ([3,6,11], index= ['averse,' baked,'f']) In [1]: S1 + s2Out [1]: a 4.0b 8.0c NaNd NaNf NaNdtype: float64 thank you for reading! This is the end of the article on "how to use Series in Pandas". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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