By the end of this pandas series tutorial, I am sure you can create and perform any task on series.<\/p>\n
A series in pandas can be thought to be the fundamental piece of data structure. It is basically nothing but a one-dimensional array-like structure, which can be used to handle and manipulate data.\u00a0What makes it special is its index attribute, which has incredible functionality and is heavily mutable.<\/p>\n Parameters in pandas series:<\/strong><\/p>\n Do you know what makes python pandas unique?<\/a><\/strong><\/em><\/p>\n Let’s start to code in pandas series-<\/strong><\/p>\n To begin, we import the pandas library.<\/p>\n In your second code box after importing the library, go ahead and enter the following code-<\/p>\n This will create your series.<\/p>\n To access the series, code the below code-<\/p>\n Output-<\/strong><\/p>\n 0\u00a0 \u00a02 Congratulations!<\/strong> <\/em>You have created your first own series in pandas.<\/p>\n We can also create a series using a ndarray<\/strong> or numpy<\/strong> array<\/strong><\/a>:<\/p>\n First, we will import the numpy library:<\/p>\n This lets us refer to the library as np. After initializing, we create a numpy array and then turn it into a series.<\/p>\n The first line creates the numpy array and the second line turns the array into pandas series.<\/p>\n Output-<\/strong><\/p>\n 0\u00a0 \u00a0d We can create a series from python<\/strong>\u00a0dictionaries<\/strong><\/a>\u00a0To do this, we first need to create a dictionary:<\/p>\n To turn this dictionary into a pandas series, all we have to do is:<\/p>\n Output-<\/strong><\/p>\n Delhi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 12.9<\/p>\n Mumbai\u00a0 \u00a0 \u00a08.4<\/p>\n Kolkata\u00a0 \u00a0 \u00a0 9.7<\/p>\n dtype: float64<\/p>\n For indexing in pandas<\/strong> series first, we will create a list.<\/p>\n This is our list, and we want this to be the index to the values (we have provided). So, we write the following code and run it:<\/p>\n Output-<\/strong><\/p>\n If you want to check the value to a corresponding index, simply use the following command<\/p>\n This will return the value 5.<\/p>\n We can use parameters to filter values in a series. For this, let\u2019s take the following example:<\/p>\n What does this mean? This basically is telling the series that you want a list of all the values that are greater than 2.<\/p>\n Running the code given above, we get:<\/p>\n n1\u00a0 4 Check out pandas basic functionality<\/a>\u00a0to enhance your skills<\/strong><\/em><\/p>\n Because 4 and 5 are the only values in the pandas series, that is more than 2. If a certain index is present inside a series or not, then use the \u2018in\u2019 parameter from python\u2019s native code.<\/p>\n This will return \u201cTrue\u201d.<\/p>\n Example of Mathematical operations on Pandas Series<\/strong><\/p>\n Output-<\/strong><\/p>\n n1\u00a0 \u00a0 20 Let\u2019s create a list of cities and implement it into a series as index:<\/p>\n Did you notice something? Chennai is a new addition and there is no value pertaining to it in the original series. Here, the value for Chennai is represented as NaN.<\/p>\n Delhi\u00a0 \u00a0 \u00a0 \u00a0 \u00a012.9 NaN is Pandas way to represent missing values.<\/p>\n Yes, it\u2019s possible to add two series in pandas.<\/p>\n Output-<\/strong><\/p>\n Chennai\u00a0 \u00a0 NaN Now, it’s time to learn how to sort in pandas series<\/a><\/strong><\/em><\/p>\n Let’s say, we want to access the first 2 elements of arr4. All we have to do is use the range function in pandas, which we can use with the help of \u2018:\u2019<\/p>\n The code to access the first two elements will be:<\/p>\n Output-<\/strong><\/p>\n Delhi\u00a0 \u00a0 \u00a0 12.9 The code for the last two is:<\/p>\n Output-<\/strong><\/p>\n Mumbai\u00a0 \u00a0 \u00a08.4 Therefore, the function basically works in the way series[x:y] where x is the number for the first row of the range and y is the last row of the range. Let\u2019s try :<\/p>\n Output-<\/strong><\/p>\n Kolkata\u00a0 \u00a0 9.7![\"Pandas](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Series.png\")
<\/a><\/p>\n1. What is pandas series?<\/h2>\n
\n
>>> import pandas as pd<\/pre>\n
2. How to create a pandas series?<\/h2>\n
>>> dataflair_arr= pd.Series([2,3,-4,6])<\/pre>\n
>>> dataflair_arr<\/pre>\n
\n1\u00a0 \u00a03
\n2\u00a0 \u00a0-4
\n3\u00a0 \u00a06
\ndtype: int64<\/p>\n<\/a><\/p>\n2.1 Create a series with ndarray or numpy array<\/h4>\n
>>> import numpy as np<\/pre>\n
>>> npa = np.array(['d','a','t','a'])\r\n>>> dataflair_ar= pd.Series(npa)\r\n>>> dataflair_ar<\/pre>\n
\n1\u00a0 \u00a0a
\n2\u00a0 \u00a0t
\n3\u00a0 \u00a0a
\ndtype: object<\/p>\n<\/a><\/p>\n2.2 Create a series from a python dictionary<\/h4>\n
>>> dataflair_dict = { 'Delhi': 12.9, 'Mumbai': 8.4, 'Kolkata': 9.7 }<\/pre>\n>>> dataflair_arr3= pd.Series(dataflair_dict)\r\n>>> dataflair_arr3<\/pre>\n
<\/a><\/p>\n3. How to change the index of pandas series?<\/h2>\n
>>> num=[\u2018n1\u2019,\u2019n2\u2019,\u2019n3\u2019,\u2019n4\u2019]<\/pre>\n
>>> dataflair_arr2= pd.Series([4,5,-2,2], index=num\r\n>>> dataflair_arr2<\/pre>\n
<\/a><\/p>\n4. How to perform mathematical operations on a series?<\/h2>\n
>>> dataflair_arr2[\u2018n2\u2019]<\/pre>\n
>>> dataflair_arr2[dataflair_arr2>2]<\/pre>\n
\nn2\u00a0 \u00a05
\ndtype: int64<\/p>\n>>> \u2018n3\u2019 in dataflair_arr2<\/pre>\n
>>> dataflair_arr2*5<\/pre>\n
\nn2\u00a0 \u00a0 25
\nn3\u00a0 \u00a0-10
\nn4\u00a0 \u00a0 10
\ndtype: int64<\/p>\n<\/a><\/p>\n5. Demonstration of missing values<\/h2>\n
>>> cities=['Delhi', 'Kolkata', 'Mumbai', 'Chennai']\r\n>>> dataflair_arr4=pd.Series(dict,index=cities)\r\n>>> dataflair_arr4<\/pre>\n
\nKolkata\u00a0 \u00a0 \u00a0 9.7
\nMumbai\u00a0 \u00a0 \u00a08.4
\nChennai\u00a0 \u00a0 NaN
\ndtype: float64<\/p>\n<\/a><\/p>\n6. How to add two series in pandas?<\/h2>\n
>>> dataflair_arr4+dataflair_arr3<\/pre>\n
\nDelhi\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 25.8
\nKolkata\u00a0 \u00a0 \u00a019.4
\nMumbai\u00a0 \u00a0 16.8
\ndtype: float64<\/p>\n<\/a><\/p>\n7. How to access a range of elements in a pandas series?<\/h2>\n
>>> dataflair_arr4[:2]<\/pre>\n
\nKolkata\u00a0 \u00a0 9.7
\ndtype: float64<\/p>\n>>> dataflair_arr4[2:]<\/pre>\n
\nChennai\u00a0 \u00a0 NaN
\ndtype: float64<\/p>\n>>> dataflair_arr4[1:3]<\/pre>\n
\nMumbai\u00a0 \u00a08.4
\ndtype: float64<\/p>\n