30 Dec Pandas Series – Attributes and Methods
The Series in Pandas is a one-dimensional array that uses the Series() method to create a Series, but it also uses different built-in attributes and methods for basic functionalities. In this lesson, let us see such attributes and methods in Python Pandas for Series:
- dtype: Return the dtype.
- ndim: Return the Number of dimensions
- size: Return the number of elements.
- name: Return the name of the Series.
- hasnans: Returns True if NaNs are in the series.
- index: The index of the series
- head(): Return the first n rows.
- tail(): Return the last n rows.
- info(): Display the Summary of the series
Let us understand them one by one:
dtype
The pandas.series.dtype is used to return the datatype of the Series.
Let us now see an example to implement the type attribute in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Datatype print("\nSeries Datatype: ", s.dtype) |
Output
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Series: 0 10 1 20 2 40 3 80 4 100 dtype: int64 Series Datatype: int64 |
ndim
The pandas.series.ndim is used to return the number of dimensions of the Series.
Let us now see an example to implement the ndim attribute in Python Pandas
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Dimensions print("\nSeries Dimensions: ", s.ndim) |
Output
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Series: 0 10 1 20 2 40 3 80 4 100 dtype: int64 Series Dimensions: 1 |
size
The pandas.series.size is used to return the number of elements in the Pandas Series.
Let us now see an example to implement the size attribute in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Return the number of elements in the Series print("\nSeries Size: ", s.size) |
Output
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Series: 0 10 1 20 2 40 3 80 4 100 dtype: int64 Series Size: 5 |
name
The pandas.series.name is used to return the name of the Series in Pandas.
Let us now see an example to implement the name attribute in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100] # Create a Series using the Series() method # We have set the Series name using the name attribute s = pd.Series(data, name ="MyNumberSeries") # Display the Series print("Series: \n", s) # Return the name of the Series print("\nSeries Name: ", s.name) |
Output
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Series: 0 10 1 20 2 40 3 80 4 100 Name: MyNumberSeries, dtype: int64 Series Name: MyNumberSeries |
hasnans
The pandas.series.hasnans attribute returns True if NaNs are in the Pandas Series.
Let us now see an example to implement the hasnans attribute in Python Pandas:
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import pandas as pd import numpy as np # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100, np.NaN] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Check whether the Series has NaNs print("\nDoes the Series has NaN? ", s.hasnans) |
Output
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Series: 0 10.0 1 20.0 2 40.0 3 80.0 4 100.0 5 NaN dtype: float64 Does the Series has NaN? True |
index
The pandas.series.index attribute is used to display the index of the Pandas Series.
Let us now see an example to implement the index attribute in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100] # Create a Series using the Series() method s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE"]) # Display the Series print("Series (with custom index labels): \n", s) # Return the index of the Series print("\nSeries Index: ", s.index) |
Output
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Series (with custom index labels): RowA 10 RowB 20 RowC 40 RowD 80 RowE 100 dtype: int64 Series Index: Index(['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], dtype='object') |
head()
The pandas.series.head() method is used to return the first n rows of the Pandas Series.
Let us now see an example to implement the head() method in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100, 200, 300, 500] # Create a Series using the Series() method s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"]) # Display the Series print("Series (with custom index labels): \n", s) # Return the first n rows. # The 5 is default for n print("\nThe first 5 rows of the series:\n", s.head()) |
Output
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Series (with custom index labels): RowA 10 RowB 20 RowC 40 RowD 80 RowE 100 RowF 200 RowG 300 RowH 500 dtype: int64 The first 5 rows of the series: RowA 10 RowB 20 RowC 40 RowD 80 RowE 100 dtype: int64 |
tail()
The pandas.series.tail() method is used to return the last n rows of the Pandas Series.
Let us now see an example to implement the tail() method in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100, 200, 300, 500] # Create a Series using the Series() method s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"]) # Display the Series print("Series (with custom index labels): \n", s) # Return the last n rows. # The 5 is default for n print("\nThe last 5 rows of the series:\n", s.tail()) |
Output
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Series (with custom index labels): RowA 10 RowB 20 RowC 40 RowD 80 RowE 100 RowF 200 RowG 300 RowH 500 dtype: int64 The last 5 rows of the series: RowD 80 RowE 100 RowF 200 RowG 300 RowH 500 dtype: int64 |
info()
The pandas.series.info() method is used to display the Summary of the Pandas Series.
Let us now see an example to implement the info() method in Python Pandas:
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import pandas as pd # Data to be stored in the Pandas Series data = [10, 20, 40, 80, 100, 200, 300, 500] # Create a Series using the Series() method s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"]) # Display the Series print("Series (with custom index labels): \n", s) # Return the summary of the series print("\nSeries Summary:\n", s.info()) |
Output
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Series (with custom index labels): RowA 10 RowB 20 RowC 40 RowD 80 RowE 100 RowF 200 RowG 300 RowH 500 dtype: int64 <class 'pandas.core.series.Series'> Index: 8 entries, RowA to RowH Series name: None Non-Null Count Dtype -------------- ----- 8 non-null int64 dtypes: int64(1) memory usage: 128.0+ bytes |
In this lesson, we saw what functionalities in the form of attributes and methods are present in Python Pandas Series.
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