Pandas Series 创建对象

# list -> Series
my_list = [1,2,3,4,np.nan,np.nan,7,8,9]
s_my_list = pd.Series(my_list)
print("s_my_list",s_my_list)

# ndarray -> series
s_nda = pd.Series(np.array(['a','b',"",'c']))
print("\n s_nda",s_nda)

# dict -> series
# a是索引
s_dict = pd.Series({"a":0,"b":5,"c":5})
print("\n s_dict",s_dict)

s_my_list 0    1.0
1    2.0
2    3.0
3    4.0
4    NaN
5    NaN
6    7.0
7    8.0
8    9.0
dtype: float64

 s_nda 0    a
1    b
2     
3    c
dtype: object

 s_dict a    0
b    5
c    5
dtype: int64

help(pd.Series)

Help on class Series in module pandas.core.series:

class Series(pandas.core.base.IndexOpsMixin, pandas.core.generic.NDFrame)
 |  One-dimensional ndarray with axis labels (including time series).
 |  
 |  Labels need not be unique but must be a hashable type. The object
 |  supports both integer- and label-based indexing and provides a host of
 |  methods for performing operations involving the index. Statistical
 |  methods from ndarray have been overridden to automatically exclude
 |  missing data (currently represented as NaN).
 |  
 |  Operations between Series (+, -, /, *, **) align values based on their
 |  associated index values-- they need not be the same length. The result
 |  index will be the sorted union of the two indexes.
 |  
 |  Parameters
 |  ----------
 |  data : array-like, dict, or scalar value
 |      Contains data stored in Series
 |  index : array-like or Index (1d)
 |      Values must be hashable and have the same length as `data`.
 |      Non-unique index values are allowed. Will default to
 |      RangeIndex(len(data)) if not provided. If both a dict and index
 |      sequence are used, the index will override the keys found in the
 |      dict.
 |  dtype : numpy.dtype or None
 |      If None, dtype will be inferred
 |  copy : boolean, default False
 |      Copy input data

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