Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). It has to be remembered that unlike Python lists, a Series will always contain data of the same type.
Let’s see how to create a Pandas Series from Dictionary.
Series() method without
In this case, dictionary keys are taken in a sorted order to construct index.
Code #1 : Dictionary keys are given in sorted order.
A 10 B 20 C 30 dtype: int64
Code #2 : Dictionary keys are given in unsorted order.
B 20 C 30 D 10 dtype: int64
Series() method with
In this case, the values in data corresponding to the labels in the index will be assigned.
Code #1 : Index list is passed of same length as the number of keys present in dictionary.
B 10 C 80 A 50 dtype: int64
Code #2 : Index list is passed of greater length than the number of keys present in dictionary in this case, Index order is persisted and the missing element is filled with NaN (Not a Number).
B 10 C 80 D NaN A 50 dtype: float64
- Creating a Pandas Series
- Creating a dataframe from Pandas series
- Creating a Pandas Series from Lists
- Python | Creating Multidimensional dictionary
- Creating a Pandas DataFrame
- Creating views on Pandas DataFrame
- Creating views on Pandas DataFrame | Set - 2
- Creating a Pandas dataframe using list of tuples
- Creating Pandas dataframe using list of lists
- Python | Creating a Pandas dataframe column based on a given condition
- Python | Pandas series.cummax() to find Cumulative maximum of a series
- Python | Pandas Series.cummin() to find cumulative minimum of a series
- Python | Pandas Series.astype() to convert Data type of series
- Python | Pandas series.cumprod() to find Cumulative product of a Series
- Python | Pandas Series.nonzero() to get Index of all non zero values in a series
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