Pandas Series is a one-dimensional labelled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called *index*.

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.

**Creating an empty Series :**

A basic series, which can be created is an Empty Series.

`# import pandas as pd ` `import` `pandas as pd ` ` ` `# Creating empty series ` `ser ` `=` `pd.Series() ` ` ` `print` `(ser) ` |

**Output :**

Series([], dtype: float64)

**Creating a series from array:**

In order to create a series from array, we have to import a numpy module and have to use array() function.

`# import pandas as pd ` `import` `pandas as pd ` ` ` `# import numpy as np ` `import` `numpy as np ` ` ` `# simple array ` `data ` `=` `np.array([` `'g'` `, ` `'e'` `, ` `'e'` `, ` `'k'` `, ` `'s'` `]) ` ` ` `ser ` `=` `pd.Series(data) ` `print` `(ser) ` |

**Output :**

**Creating a series from array with index :**

In order to create a series from array with index, we have to provide index with same number of element as it is in array.

`# import pandas as pd ` `import` `pandas as pd ` ` ` `# import numpy as np ` `import` `numpy as np ` ` ` `# simple array ` `data ` `=` `np.array([` `'g'` `, ` `'e'` `, ` `'e'` `, ` `'k'` `, ` `'s'` `]) ` ` ` `# providing an index ` `ser ` `=` `pd.Series(data, index ` `=` `[` `10` `, ` `11` `, ` `12` `, ` `13` `, ` `14` `]) ` `print` `(ser) ` |

**Output :**

**Creating a series from Lists:**

In order to create a series from list, we have to first create a list after that we can create a series from list.

`import` `pandas as pd ` ` ` `# a simple list ` `list` `=` `[` `'g'` `, ` `'e'` `, ` `'e'` `, ` `'k'` `, ` `'s'` `] ` ` ` `# create series form a list ` `ser ` `=` `pd.Series(` `list` `) ` `print` `(ser) ` |

**Output :**

**Creating a series from Dictionary:**

In order to create a series from dictionary, we have to first create a dictionary after that we can make a series using dictionary. Dictionary key are used to construct a index.

`import` `pandas as pd ` ` ` `# a simple dictionary ` `dict` `=` `{` `'Geeks'` `: ` `10` `, ` ` ` `'for'` `: ` `20` `, ` ` ` `'geeks'` `: ` `30` `} ` ` ` `# create series from dictionary ` `ser ` `=` `pd.Series(` `dict` `) ` ` ` `print` `(ser) ` |

**Output :**

**Creating a series from Scalar value:**

In order to create a series from scalar value, an index must be provided. The scalar value will be repeated to match the length of index.

`import` `pandas as pd ` ` ` `import` `numpy as np ` ` ` `# giving a scalar value with index ` `ser ` `=` `pd.Series(` `10` `, index ` `=` `[` `0` `, ` `1` `, ` `2` `, ` `3` `, ` `4` `, ` `5` `]) ` ` ` `print` `(ser) ` |

**Output :**

**Creating a series using NumPy functions :**

In order to create a series using numpy function, we can use different function of numpy like numpy.linspace(), numpy.random.radn().

`# import pandas and numpy ` `import` `pandas as pd ` `import` `numpy as np ` ` ` `# series with numpy linspace() ` `ser1 ` `=` `pd.Series(np.linspace(` `3` `, ` `33` `, ` `3` `)) ` `print` `(ser1) ` ` ` `# series with numpy linspace() ` `ser2 ` `=` `pd.Series(np.linspace(` `1` `, ` `100` `, ` `10` `)) ` `print` `(` ```
"
"
``` `, ser2) ` |

**Output :**

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