## pandas series

A Pandas series is a data structure that stores data in the form of a column.It is a one-dimensional array holding data of any type.

### pd series

`pandas.Series( data, index, dtype, copy)`

Parameter | Description |
---|---|

data | array like – Contains data stored in Series |

index | Index values must be unique and hashable, same length as data |

dtype | It is for data type. If None, data type will be inferred |

copy | Copy data. Default `False` |

Imports the Pandas module and then calls the `Series()`

class constructor to create an empty series.

`import pandas as pd mySeries = pd.Series()`

You can also create a series using a NumPy array. But, first you need to pass the array to the `Series()`

class constructor.

`import pandas as pd import numpy as np myArray = np.array([10, 20, 30, 40, 50]) mySeries = pd.Series(myArray) print(mySeries)`

**Output**

`0 10 1 20 2 30 3 40 4 50 dtype: int64`

In the Above Output, You can see that the indexes for a series starts from , You can also define custom indexes for your series. To do so, you need to pass your list of indexes to the index attribute of the Series class.

`import pandas as pd import numpy as np myArray = np.array([10, 20, 30, 40, 50]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(mySeries)`

**Output**

`A 10 B 20 C 30 D 40 E 50 dtype: int64`

We passed the index values here. Now we can see the customized indexed values in the output.

#### Create a Series from dict

- You can also create a series using a dictionary.
- If index is passed, the values in data corresponding to the labels in the index will be printed out.
- If no index is specified, the dictionary keys will become series indexes while the dictionary values are inserted as series items.

`import pandas as pd myDict = { 'A' :1, 'B' :2, 'C' :3, 'D': 4, 'E': 5 } mySeries = pd.Series(myDict) print(mySeries)`

**Output**

`A 1 B 2 C 3 D 4 E 5 dtype: int64`

`import pandas as pd myDict = { 'A' :1, 'B' :2, 'C' :3, 'D': 4, 'E': 5 } mySeries = pd.Series(myDict, index = ['B', 'G' ,'D', 'E', 'Z']) print(mySeries)`

**Output**

`B 2.0 G NaN D 4.0 E 5.0 Z NaN dtype: float64`

#### Create a Series from Scalar

- If data is a scalar value, an index must be provided.

`import pandas as pd mySeries = pd.Series(44, index = ["A", "B", "C", "D","E"]) print(mySeries)`

**Output**

`A 44 B 44 C 44 D 44 E 44 dtype: int64`

#### Accessing Items

We can Access the Items , You can use square brackets as well as index labels to access series items.

`import pandas as pd import numpy as np myArray = np.array([10, 20, 30, 40, 50]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(mySeries[0]) print(mySeries['D'])`

**Output**

`10 40`

Get the first two elements in the Series.

`import pandas as pd import numpy as np myArray = np.array([10, 20, 30, 40, 50]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(mySeries[:2])`

**Output**

`A 10 B 20 dtype: int64`

#### Finding Maximum and Minimum Values

We can find the maximum and minimum values, respectively, from a series using `min()`

and `max()`

functions from the NumPy module.

`import pandas as pd import numpy as np myArray = np.array([10, 20, 30, 40, 50]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(np.min(mySeries)) print(np.max(mySeries))`

**Output**

`10 50`

#### Finding Mean and Median

The `mean()`

method from the numpy module can help us find the mean of a given Pandas series.

`import pandas as pd import numpy as np myArray = np.array([5, 3 , 7 , 11, 15]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(np.mean(mySeries))`

**Output**

`8.2`

The `median()`

method from the numpy module can help us find the median of a given Pandas series.

`import pandas as pd import numpy as np myArray = np.array([5, 3 , 7 , 11, 15]) mySeries = pd.Series(myArray, index = ["A", "B", "C", "D","E"]) print(np.median(mySeries))`

**Output**

`7.0`