Convert a series of date strings to a time series in Pandas Dataframe Last Updated : 18 Aug, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report During the analysis of a dataset, oftentimes it happens that the dates are not represented in proper type and are rather present as simple strings which makes it difficult to process them and perform standard date-time operations on them. pandas.to_datetime() Function helps in converting a date string to a python date object. So, it can be utilized for converting a series of date strings to a time series. Let's see some examples:Example 1: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['28 July 2020', '16 January 2013', '29 February 2016 18:14']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after being # converted to a time series print("\nSeries of date strings after" + " being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Example 2: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['2020/07/28', '2013/01/16', '2016/02/29 18:14']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after being # converted to a time series print("\nSeries of date strings after " + "being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Example 3: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['2020-07-28', '2013-01-16', '2016-02-29 18:14']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after # being converted to a time series print("\nSeries of date strings after " + "being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Example 4: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['28/07/2020', '01/16/2013', '29/02/2016 18:14']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after being # converted to a time series print("\nSeries of date strings after " + "being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Example 5: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['20200728', '20130116', '20160229 181431']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after # being converted to a time series print("\nSeries of date strings after " + "being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Example 6: Python3 # import pandas library import pandas as pd # create a series of date strings dt_series = pd.Series(['28 July 2020', '2013-01-16', '20160229 18:14', '5/03/2019 2215', '20151204 09:23']) # display the series initially print("Series of date strings:") print(dt_series) # display the series after # being converted to a time series print("\nSeries of date strings after " + "being converted to a timeseries:") print(pd.to_datetime(dt_series)) Output: Comment More infoAdvertise with us Next Article Make a Pandas DataFrame with two-dimensional list | Python A amarjeet_singh Follow Improve Article Tags : Python Python-pandas Python pandas-dataFrame Python Pandas-exercise Practice Tags : python Similar Reads Pandas Exercises and Programs Pandas is an open-source Python Library that is made mainly for working with relational or labelled data both easily and intuitively. 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I 2 min read How to Get Column Names in Pandas DataframeWhile analyzing the real datasets which are often very huge in size, we might need to get the pandas column names in order to perform certain operations. The simplest way to get column names in Pandas is by using the .columns attribute of a DataFrame. Let's understand with a quick example:Pythonimpo 4 min read How to rename columns in Pandas DataFrameIn this article, we will see how to rename column in Pandas DataFrame. The simplest way to rename columns in a Pandas DataFrame is to use the rename() function. This method allows renaming specific columns by passing a dictionary, where keys are the old column names and values are the new column nam 4 min read Collapse multiple Columns in PandasWhile operating dataframes in Pandas, we might encounter a situation to collapse the columns. Let it be cumulated data of multiple columns or collapse based on some other requirement. Let's see how to collapse multiple columns in Pandas. 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Solution #1: We can use simple indexing operation to select all those values in the column which satisfies the given condition. Python3 # importing pandas as pd import pandas as pd # Creat 2 min read Using dictionary to remap values in Pandas DataFrame columnsWhile working with data in Pandas, we often need to modify or transform values in specific columns. One common transformation is remapping values using a dictionary. This technique is useful when we need to replace categorical values with labels, abbreviations or numerical representations. In this a 4 min read Formatting float column of Dataframe in PandasWhile presenting the data, showing the data in the required format is also a crucial part. Sometimes, the value is so big that we want to show only the desired part of this or we can say in some desired format. Let's see different methods of formatting integer columns and the data frame it in Pandas 3 min read Create a New Column in Pandas DataFrame based on the Existing ColumnsWhen working with data in Pandas, we often need to change or organize the data into a format we want. One common task is adding new columns based on calculations or changes made to the existing columns in a DataFrame. In this article, we will be exploring different ways to do that.Task: We have a Da 4 min read Python | Creating a Pandas dataframe column based on a given conditionWhile operating on data, there could be instances where we would like to add a column based on some condition. There does not exist any library function to achieve this task directly, so we are going to see how we can achieve this goal. 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Python3 # import pandas as pd import pandas as pd gapminder_csv_url ='http://bit.ly/2cLzoxH' # 2 min read Split a String into columns using regex in pandas DataFrameGiven some mixed data containing multiple values as a string, let's see how can we divide the strings using regex and make multiple columns in Pandas DataFrame. Method #1: In this method we will use re.search(pattern, string, flags=0). Here pattern refers to the pattern that we want to search. It ta 3 min read Count Frequency of Columns in Pandas DataFrameWhen working with data in Pandas counting how often each value appears in a column is one of the first steps to explore our dataset. This helps you understand distribution of data and identify patterns. 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Python3 # import Pandas as pd import pandas as p 3 min read Difference of two columns in Pandas dataframeDifference of two columns in pandas dataframe in Python is carried out by using following methods : Method #1 : Using †-†operator. Python3 import pandas as pd # Create a DataFrame df1 = { 'Name':['George','Andrea','micheal', 'maggie','Ravi','Xien','Jalpa'], 'score1':[62,47,55,74,32,77,86], 'score2 2 min read Get the index of maximum value in DataFrame columnPandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we get the index of maximum value in DataFrame column.Observe this dataset first. We'll use 'Weight' and 'Salary' columns of this data in order t 2 min read Get the index of minimum value in DataFrame columnPandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we get the index of minimum value in DataFrame column. Observe this dataset first. We'll use 'Weight' and 'Salary' columns of this data in order 2 min read Get n-largest values from a particular column in Pandas DataFramePandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we can get n-largest values from a particular column in Pandas DataFrame. Observe this dataset first. We’ll use ‘Age’, ‘Weight’ and ‘Salary’ colu 1 min read Get n-smallest values from a particular column in Pandas DataFramePandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we can get n-smallest values from a particular column in Pandas DataFrame. Observe this dataset first. 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In this guide we will explore two simple 2 min read Creating a Pandas Series from DictionaryA Pandas Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). It has to be remembered that, unlike Python lists, a Series will always contain data of the same type. Let’s see how to create a Pandas Series from P 2 min read Creating a Pandas Series from ListsA Pandas Series is a one-dimensional labeled array capable of holding various data types such as integers, strings, floating-point numbers and Python objects. Unlike Python lists a Series ensures that all elements have the same data type. It is widely used in data manipulation and analysis.In this a 3 min read Create Pandas Series using NumPy functionsA Pandas Series is like a one-dimensional array which can store numbers, text or other types of data. NumPy has built-in functions that generate sequences, random values or repeated numbers In this article, we'll learn how to create a Pandas Series using different functions from the NumPy library.Me 2 min read Access the elements of a Series in PandasPandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Labels need not be unique but must be a hashable type. Let's discuss different ways to access the elements of given Pandas Series. First create a Pandas Series. Python 2 min read Pandas Date and Time Practice ExerciseBasic of Time Series Manipulation Using PandasAlthough the time series is also available in the Scikit-learn library, data science professionals use the Pandas library as it has compiled more features to work on the DateTime series. We can include the date and time for every record and can fetch the records of DataFrame. We can find out the da 4 min read Using Timedelta and Period to create DateTime based indexes in PandasReal life data often consists of date and time-based records. From weather data to measuring some other metrics in big organizations they often rely on associating the observed data with some timestamp for evaluating the performance over time. We have already discussed how to manipulate date and tim 3 min read Convert column type from string to datetime format in Pandas dataframeTo perform time-series operations, dates should be in the correct format. Let's learn how to convert a Pandas DataFrame column of strings to datetime format. Pandas Convert Column To DateTime using pd.to_datetime()pd.to_datetime() function in Pandas is the most effective way to handle this conversio 4 min read DataFrame String ManipulationExtract punctuation from the specified column of Dataframe using RegexPrerequisite: Regular Expression in Python In this article, we will see how to extract punctuation used in the specified column of the Dataframe using Regex. 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We can change them from Integers to Float type, Integer to String, String to Integer, Float to String, etc. There are three methods 4 min read Accessing and Manipulating Data in DataFrameAccess Index of Last Element in pandas DataFrame in PythonIn this article, we are going to see how to access an index of the last element in the pandas Dataframe. To achieve this, we can use Dataframe.iloc, Dataframe.iget, and Dataframe.index. let's go through all of them one by one.  Dataframe.iloc - Pandas Dataframe.iloc is used to retrieve data by spe 3 min read Replace Characters in Strings in Pandas DataFrameIn this article, we are going to see how to replace characters in strings in pandas dataframe using Python. We can replace characters using str.replace() method is basically replacing an existing string or character in a string with a new one. we can replace characters in strings is for the entire 3 min read Replace values of a DataFrame with the value of another DataFrame in PandasIn this article, we will learn how we can replace values of a DataFrame with the value of another DataFrame using pandas. It can be done using the DataFrame.replace() method. It is used to replace a regex, string,  list, series, number, dictionary, etc. from a DataFrame, Values of the DataFrame met 4 min read Replace negative values with latest preceding positive value in Pandas DataFrameIn this article, we will discuss how to replace the negative value in Pandas DataFrame Column with the latest preceding positive value. While doing this there may arise two situations - Value remains unmodified if no proceeding positive value existsValue update to 0 if no proceeding positive value 3 min read How to add column from another DataFrame in Pandas ?In this discussion, we will explore the process of adding a column from another data frame in Pandas. Pandas is a powerful data manipulation library for Python, offering versatile tools for handling and analyzing structured data. Add column from another DataFrame in Pandas There are various ways to 6 min read DataFrame Visualization and ExportingHow to render Pandas DataFrame as HTML Table?Pandas in Python can convert a Pandas DataFrame to a table in an HTML web page. The pandas.DataFrame.to_html() method is used to render a Pandas DataFrame into an HTML format, allowing for easy display of data in web applications. In this article, we will understand how to use the Styler Object and 6 min read Exporting Pandas DataFrame to JSON FilePandas a powerful Python library for data manipulation provides the to_json() function to convert a DataFrame into a JSON file and the read_json() function to read a JSON file into a DataFrame.In this article we will explore how to export a Pandas DataFrame to a JSON file with detailed explanations 2 min read Create and display a one-dimensional array-like object using Pandas in PythonSeries() is a function present in the Pandas library that creates a one-dimensional array and can hold any type of objects or data in it. In this article, let us learn the syntax, create and display one-dimensional array-like object containing an array of data using Pandas library. pandas.Series() S 2 min read Export Pandas dataframe to a CSV fileWhen working on a Data Science project one of the key tasks is data management which includes data collection, cleaning and storage. Once our data is cleaned and processed it’s essential to save it in a structured format for further analysis or sharing.A CSV (Comma-Separated Values) file is a widely 2 min read Display the Pandas DataFrame in Heatmap stylePandas library in the Python programming language is widely used for its ability to create various kinds of data structures and it also offers many operations to be performed on numeric and time-series data. By displaying a panda dataframe in Heatmap style, the user gets a visualisation of the numer 6 min read Like