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--- pandas_series [2021/02/04 19:55] – created raju
+++ pandas_series [2024/02/06 05:11] – raju
@@ Line 1: / Line 1: @@
+===== creating a series =====
+==== create a series from a list ====
+<code>
+>>> a = pd.Series(['sun', 'mon', 'tue'])
+>>> a
+    sun
+    mon
+    tue
+dtype: object
+</code>
+To assign an index
+<code>
+>>> b = pd.Series(['sun', 'mon', 'tue'], index=['s', 'm', 't'])
+>>> b
+s    sun
+m    mon
+t    tue
+dtype: object
+</code>
+To assign a name to the column
+<code>
+>>> c = pd.Series(['sun', 'mon', 'tue'], index=['s', 'm', 't'], name='day')
+>>> c
+s    sun
+m    mon
+t    tue
+Name: day, dtype: object
+</code>
+To assign a name to the index
+<code>
+>>> d = pd.Series(['sun', 'mon', 'tue'], index=['s', 'm', 't'], name='day')
+>>> d.index.name = 'letter'
+>>> d
+letter
+s    sun
+m    mon
+t    tue
+Name: day, dtype: object
+</code>
+Column name is useful when converting the series to dataframe.
+<code>
+>>> b.to_frame()
+s  sun
+m  mon
+t  tue
+>>> c.to_frame()
+   day
+s  sun
+m  mon
+t  tue
+</code>
+If the series did not have a name to begin with but we desire to have one while converting to the dataframe
+<code>
+>>> b.to_frame(name='days')
+  days
+s  sun
+m  mon
+t  tue
+</code>
+The index name comes in handy while resetting the index
+<code>
+>>> c.reset_index()
+  index  day
+     s  sun
+     m  mon
+     t  tue
+>>> d.reset_index()
+  letter  day
+      s  sun
+      m  mon
+      t  tue
+</code>
+===== dummy =====
 ==== append element to series ====
 <code>
@@ Line 19: / Line 101: @@
 '1.2.1'
 </code>
+==== return a random element ====
+Use pandas.Series.sample
+Ref:-
+  * https://pandas.pydata.org/docs/reference/api/pandas.Series.sample.html
+===== check if =====
+==== check if a series is empty ====
+Use pandas.Series.empty .
+<code>
+$ ipython
+In [1]:
+import pandas as pd
+import numpy as np
+df1 = pd.DataFrame({'A':  []})
+df1
+Out[1]:
+Empty DataFrame
+Columns: [A]
+Index: []
+In [2]:
+df1['A'].empty
+Out[2]:
+True
+</code>
+A series with just NaNs is considered "non-empty". Drop the NaNs to make it "empty".
+<code>
+In [3]:
+df2 = pd.DataFrame({'A':  [np.nan]})
+df2
+Out[3]:
+    A
+NaN
+In [4]:
+df2['A'].empty
+Out[4]:
+False
+In [5]:
+df2['A'].dropna().empty
+Out[5]:
+True
+</code>
+Used Python 3.9.4 and IPython 7.22.0
+tags | check if a series has at least one element
+==== check if all elements in a series are unique ====
+Use pandas.Series.is_unique
+<code>
+In [1]:
+import pandas as pd
+In [2]:
+pd.Series([1, 2, 3]).is_unique
+Out[2]:
+True
+In [3]:
+pd.Series([1, 2, 2]).is_unique
+Out[3]:
+False
+</code>
+Missing values are treated as any other value. So if there are multiple NaNs, it will return True. If this is not desired, drop the NaNs first.
+<code>
+In [4]:
+import numpy as np
+pd.Series([1, 2, 3, np.nan, np.nan]).is_unique
+Out[4]:
+False
+In [5]:
+pd.Series([1, 2, 3, np.nan, np.nan]).dropna().is_unique
+Out[5]:
+True
+</code>
+For completeness
+<code>
+In [6]:
+pd.Series([1, 2, 2, np.nan, np.nan]).is_unique
+Out[6]:
+False
+In [7]:
+pd.Series([1, 2, 2, np.nan, np.nan]).dropna().is_unique
+Out[7]:
+False
+</code>
+Using | pandas 1.5.3, python 3.11.4, ipython 8.12.0
+Ref:-
+  * https://pandas.pydata.org/docs/reference/api/pandas.Series.is_unique.html
+  * https://stackoverflow.com/questions/48838247/how-to-check-every-pandas-series-value-is-unique