Pandas Series
pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)
Pandas Series represents a one-dimensional ndarray with axis labels. It is not necessary for the labels to be unique but they should be hashtable type. Series supports both integer-based indexing and non-integer based indexing. A Series has two columns, first for the index and the second for the actual data. Series has the following parameters,
Data
The data can be array-like, Iterable, dict, or scalar value
Index
It can be array-like or Index (1-dimension). The values must be hashtable. It must be of the same length as data. If index is not passed, by default np.arrange(n) is applied.dtype
It can be str, NumPy.dtype, or ExtensionDtype. This field defines the data type for the output Series. If it is not defined, this will be inferred from the data. This field is optional.Copy
This field is used for copying the data.Creating a Series from pandas
Create an empty series
import pandas as pd # import Pandas # Generate the empty series a = pd.Series() print(a) #output Series([], dtype: float64)
Create a series with a list
import pandas as pd # import Panda # Generate the series with Data, and Index index =[1, 3, 4, 5, 6, 2, 9] Data =['a', 'b', 'c', 'd', 'e', 'f', 'g'] a = pd.Series(Data, index = index) print(a) #output 1 a 3 b 4 c 5 d 6 e 2 f 9 g dtype: object
Creating a series with a dictionary
import pandas as pd # import Pandas # Generate the series with a dictionary data dct ={'red':1, 'blue':2, 'green':3, 'orange':4, 'purple':5} a = pd.Series(dct) print(a) #output red 1 blue 2 green 3 orange 4 purple 5 dtype: int64
Creating a series with Numpy.ndarray
import pandas as pd import numpy as np # import Pandas # Generate the series with numpy arr=np.array([10, 20, 30, 40]) a = pd.Series(arr) print(a) #output 0 10 1 20 2 30 3 40 dtype: int64
Or, we can provide the index explicitly,
import pandas as pd import numpy as np # import Pandas # Generate the series with numpy arr=np.array([10, 20, 30, 40]) index=['a','b','c','d'] a = pd.Series(arr, index=index) print(a) #output a 10 b 20 c 30 d 40 dtype: int64
Create a series with a scaler
import pandas as pd # import Pandas # Generate the series with a scaler index=['a','b','c','d'] a = pd.Series(5.0, index=index) print(a) #output a 5.0 b 5.0 c 5.0 d 5.0 dtype: float64
Accessing element by index
The elements can be accessed by the label or index position.
import pandas as pd import numpy as np # import Pandas index=['a','b','c','d'] arr=np.array([20, 50, 60, 80]) s = pd.Series(arr, index=index) print(s['b']) #accessing elements in a series print(s[0]) #accessing elements in a series #output 50 20
Series Object Attributes
These attributes return information about the series. For example
- Series.T returns the transpose, It is basically its own definition.
- Series.array returns the ExtensionArray of the data backing this Series or Index
- Series.shape defines a tuple of shape of the data.
- Series.dtype defines the data type of the data.
- Series.size defines the size of the data.
- Series.index defines the index of the Series.
- Series.empty defines True if Series object is empty, otherwise returns false.
- Series.hasnans returns a boolean value True if there are any NaN values, otherwise returns false.
- Series.nbytes defines the number of bytes in the data.
- Series.ndim defines the number of dimensions in the data.
- Series.is_monotonic returns a boolean value if the values in the object are monotonic_increasing.
- Series.is_monotonic_decreasing returns a boolean value if values in the object are monotonically decreasing.
- Series.is_monotonic_increasing returns a boolean value if values in the object are monotonically increasing.
- Series.at can be used to access a single value for a row/column label pair.
- Series.itemsize defines the size of the datatype of item.
import pandas as pd import numpy as np # import Pandas index=['a','b','c','d'] arr=np.array([20, 50, 60, 80]) s = pd.Series(arr, index=index) print(s.index) print(s.values) print(s.ndim) print(s.dtype) #output Index(['a', 'b', 'c', 'd'], dtype='object') [20 50 60 80] 1 int64
Series Object methods
There are several methods provides to perform several operations using the series.
