The pandas series.last() method is used to return final periods based on the date offset. By applying this series.last() method we can get the final periods of the time series object. The last method is very similar to the pandas series.first() method, here we can get the final periods instead of the initial periods.The series.last() method has a parameter called offset which is used to mention the length of the offset data to select the rows within the given limit.The last() method will return a now Series object with resultant rows and it will raise the TypeError if the index ... Read More

The pandas series.last_valid_index() method is used to get the index of the last valid element from the given series object. This means the last_valid_index() method returns the index of the last non_null element of the series.It will return a single scalar based on the type of series index, and it will return None if the given series has all null/NA values or if it is empty. The method doesn’t have any parameters.Example 1Let’s create a pandas series object and retrieve the last valid index by using the last_valid_index() method.# importing packages import pandas as pd import numpy as np ... Read More

The pandas series.first_valid_index() method is used to get the index of the first valid data. This means the first_valid_index() method returns the index of the first non_null element of the series.It will return a single scalar based on the type of series index, and it will return None if the given series has all null/NA values or if it is empty. The first_valid_index() method doesn’t take any parameter.Example 1Let’s take a series object and try to retrieve the first valid index.# importing packages import pandas as pd import numpy as np # create a series s = pd.Series([None, np.nan, ... Read More

The pandas series.first() method is supposed to return initial periods based on the dates. By applying this method we can get the initial periods of the time series data based on a date offset.It has a parameter called offset and also we can mention the length of the offset data to select the rows within the limit.The first() method will return a now Series object with resultant rows and it will raise the TypeError if the index of the input series object doesn’t have the DatetimeIndex.Example 1In this following example, a series “s” is created by using the pandas DateTime ... Read More

By using the regex parameter we can apply the regular expression to the filter() method and this helps to retrieve the rows of the series object. The basic working of series.filter() method in pandas series constructor is used to subset the rows of a series object based on the index labels.The parameter regex is used to define a search pattern (regular expression) that is used to retrieve the resultant rows.Example 1In this following example, we have created a series object using a list of integers and the index labels are created by using the pandas data range function.# importing pandas ... Read More

The series.filter() method in the pandas series constructor is used to subset the rows of a series object based on the index labels. The filter method does not work for the content of the series object, it is only applied to the index labels of the series object.The method won’t raise an error if the specified label does not match to the series index labels.The parameters for the filter() method are items, like, regex, and axis. The items parameter takes a list-like object to access the set of rows from the given series object. The regex parameter is used to ... Read More

The series.expanding() method is one of the window methods of pandas and it Provides expanding transformations. And it returns a window subclassed for the particular operation.The parameters for this method are min_periods, center, axis, and method. The default value for the min_periods is 1 and it also takes an integer value. The center parameter takes a boolean value and the default one is False. In the same way, the default value for the axis parameter is 0, and for the method is ‘single’.Example 1In this following example, the series.expanding() method calculated the cumulative sum of the entire series object# importing ... Read More

The pandas series.ffill() method works equal to the series.fillna() with “method = ffill” function or we can say that the series.ffill() is a synonym for the forward fill method.The series.ffill() method replaces the Nan or NA values in the given series object using the forward fill method. The parameters for this method are inplace, axis, limit, and downcast.It does not have parameters like value and method. Because it takes the series element as a replacement value and fills the missing values using the forward fill method.Example 1In this following example, we have applied the ffill() method to the series object ... Read More

In the process of pandas data cleaning, replacing missing values takes a very important role and in some conditions we must replace those missing values with the mean of series elements. This can be done by using the fillna() method.The basic operation of this pandas series.fillna() method is used to replace missing values (Nan or NA) with a specified value. Initially, the method verifies all the Nan values and replaces them with the assigned replacement value.Example 1Here we will see how the series.fillna() method replaces the missing value with mean.# importing pandas package import pandas as pd import numpy as ... Read More

The pandas series.fillna() method is used to replace missing values with a specified value. This method replaces the Nan or NA values in the entire series object.The parameters of pandas fillna are as follows −Value − it allows us to specify a particular value to replace Nan’s, by default it takes None.Method − it is used to fill the missing values in the reindexed Series. It takes any of these values like ‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, and None(default).Inplace − this parameter takes a boolean value. If it takesTrue, then the modifications are applied to the original series object itself, otherwise, ... Read More