Spark Combine Two Dataframes With Different Columns


See the Deploying subsection below. Regarding your post "SQL: If Exists Update Else Insert" with the alternative method of doing the Update and then checking the @@ROWCOUNT as to whether to perform an insert or not…. Especially in the web development world, you'll likely encounter JSON through one of the many REST APIs, application configuration, or even simple data storage. What is a merge or join of two dataframes? What are inner, outer, left and right merges? How do I merge two dataframes with different common column names? (left_on and right_on syntax) If you’d like to work through the tutorial yourself, I’m using a Jupyter notebook setup with Python 3. There are two ways you can do so. The data frames have several columns with the same name, and each > has a different number of rows. Let's get it going. Apache Spark Foundation Course Spark Dataframe transformations video training by Learning Journal. 6 Differences Between Pandas And Spark DataFrames. A way to Merge Columns of DataFrames in Spark with no Common Column Key March 22, 2017 Made post at Databricks forum, thinking about how to take two DataFrames of the same number of rows and combine, merge, all columns into one DataFrame. Spark's new DataFrame API is inspired by data frames in R and Python (Pandas), but designed from the ground up to support modern big data and data science applications. In this article I will illustrate how to merge two dataframes with different schema. In my first two blog posts of the Spark Streaming and Kafka series - Part 1 - Creating a New Kafka Connector and Part 2 - Configuring a Kafka Connector - I showed how to create a new custom Kafka Connector and how to set it up on a Kafka. Note the $ syntax; you select columns of a data frame by using a dollar sign and the name of the column. Key topics covered here: • What is a merge or join of two dataframes? • What are inner, outer, left and right merges? • How do I merge two dataframes with different common column names? (left_on and right. Column = id Beside using the implicits conversions, you can create columns using col and column functions. Spark SQL is a Spark module for structured data processing. join (self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False) [source] ¶ Join columns of another DataFrame. One option to concatenate string columns in Spark Scala is using concat. Combine data. Instead of having an numpy array, list of arrays, or matrix as input, the function works on Spark DataFrames with a single column, a list of single-column Spark DataFrames, or a SparkDataframe with multiple columns. Say, for instance, ORDER_DATE is a timestamp column. These functions allow crossing the data in a number of ways and avoid explicit use of loop constructs. Python also provides some built-in data types, in particular, dict, list, set and frozenset, and tuple. Apache Spark Foundation Course Spark Dataframe transformations video training by Learning Journal. In my opinion, however, working with dataframes is easier than RDD most of the time. Pardon, as I am still a novice with Spark. Much of Spark's power lies in its ability to combine very different techniques and processes into a single, coherent whole. How to merge two dataframes with same schema This post has NOT been accepted by the mailing list yet. Here's a pyspark solution. The rows in the two data frames that match on the specified columns are extracted, and joined together. ) An example element in the 'wfdataserie. Here I am going to show just some basic pandas stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. Therefore, I would like to share my experiences here and give an easy introduction for combining DataFrames. This does not mean that the columns are the index of the DataFrame. converting character vectors to factors). DataFrame provides indexing labels loc & iloc for accessing the column and rows. Each argument can either be a Spark DataFrame or a list of Spark DataFrames When row-binding, columns are matched by name, and any missing columns with be filled with NA. Merging strings that have identical rownames in a dataframe | Post 302882588 by Alyaa on Wednesday 8th of January 2014 03:59:23 AM The UNIX and Linux Forums Forums. When row-binding, columns are matched by name, and any missing columns with be filled with NA. So that you can access the results, you need to alias the DataFrames to different names—otherwise you will be unable to select the columns due to name collision (see Example 4-10). To create a DataFrame, you can choose to start from scratch or convert other data structures like Numpy arrays into a DataFrame. Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. I've two dataframes. I have looked into sqlContext documentation but there is nothing on how to merge two data-frames. This is useful when you need to drill down to…. SQL LEFT JOIN Keyword. Stanford CS246H Winter 2019 Example take Action usersDF sparkreadjsonusersjson from CS 246H at Stanford University. R has lots of handy functionality for merging and appending multiple dataframes. AWS Glue makes it easy to schedule recurring ETL jobs, chain multiple jobs together, or invoke jobs on-demand from other services like AWS Lambda. concat ([df_a Merge two dataframes with both the left and right dataframes using. Since DataFrames are inherently multidimensional, we must invoke two methods of summation. Apache Spark, Parquet, and Troublesome Nulls. There are two ways you can do so. Consequently, we see our original unordered output, followed by a second output with the data sorted by column z. left− Dataframe1. Now this here is how we tell Spark SQL which column should behave as keys. The Multi-index of a pandas DataFrame. Create DataFrames from a list of the rows. Introduction to DataFrames - Python; Introduction to DataFrames - Scala. We'll move on to cover DataFrames and Datasets, which give us a way to mix RDDs with the powerful automatic optimizations behind Spark SQL. merge() function. In order to merge the Dataframes we need to identify a column common to both of them. StructType objects define the schema of Spark DataFrames. In many Spark applications a common user scenario is to add an index column to each row of a Distributed DataFrame (DDF) during data preparation or data transformation stages. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. The Good, the Bad and the Ugly of dataframes. Merging multiple data frames row-wise in PySpark. , adding columns of second dataframe to the first dataframe with respect to a common column(s), you can use merge() function. Therefore, we use left_on and right_on to replace the method on as shown below. I am trying to merge two data frames, but one of the column headings are different in the two frames. [/code]The one that has usingColumns (Seq[String]) as second parameter works best, as the columns that you join on won’t be duplicate. Add this suggestion to a batch that can be applied as a single commit. import matplotlib. Tibbles are a modern take on data frames. In contrast, a global view is visible across multiple SparkSessions within a Spark application. In this video, I'll demonstrate how to do this using two different logical operators. It requires two DataFrames and merges the content based on common columns values. Is there any function in spark sql to do the same? Announcement! Career Guide 2019 is out now. # When merging two data frames that do not have matching column names, we can # use the by. The advantage of HBase is that you can define columns on the fly, put attribute names in column qualifiers, and group data by column families. A different set of attributes represents a different type of object, and thus belongs in a different table. Comparing Spark Dataframe Columns. 4 and above. We are thus led to believe there was a perfect match between the index of the left dataframe and the "key" column of the right dataframe ('d' here). In many situations, we split the data into sets and we apply some functionality on each subset. Not that Spark doesn't support. A dataframe is a two-dimensional data structure having multiple rows and columns. Enter the iPython shell. This can be achieved in multiple ways:. The below is the code to create a spark session. Explore libraries to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow. Pipelining is as simple as combining multiple transformations together. Observations in Spark DataFrame are organised under named columns, which helps Apache Spark to understand the schema of a DataFrame. In many "real world" situations, the data that we want to use come in multiple files. Spark DataFrames for large scale data science | Opensource. But from Spark 2. How to concatenate/append multiple Spark dataframes column wise in Pyspark? columns to each data frame, join two data frames and delete those temp columns after. Spark provides the Dataframe API, which is a very powerful API which enables the user to perform parallel and distrivuted structured data processing on the input data. Merge, join, and concatenate; Reshaping and Pivot Tables; Working with Text Data; Working with missing data; Categorical Data; Nullable Integer Data Type; Visualization; Computational tools; Group By: split-apply-combine; Time Series / Date functionality; Time Deltas; Styling; Options and Settings; Enhancing Performance; Sparse data structures. columns) in order to ensure both df have the same column order before the union. Introduction to DataFrames - Python. Each argument can either be a Spark DataFrame or a list of Spark DataFrames. In pandas, you can select multiple columns by their name, but the column name gets stored as a list of the list that means a dictionary. id: Data frame identifier. when the dataframes to combine do not have the same order of columns, it is better to df2. Below is the implementation using Numpy and Pandas. By the end of this post, you should be familiar on performing the most frequently data manipulations on a spark dataframe. SQL LEFT JOIN Keyword. Tibbles are a modern take on data frames. Just read the two data frames into R. corrwith (self, other, axis=0, drop=False, method='pearson') [source] ¶ Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame. Spark DataFrames are very interesting and help us leverage the power of Spark SQL and combine its procedural paradigms as needed. merge is a generic function whose principal method is for data frames: the default method coerces its arguments to data frames and calls the "data. Over the last 5-10 years, the JSON format has been one of, if not the most, popular ways to serialize data. Combining the results. Koalas: pandas API on Apache Spark¶ The Koalas project makes data scientists more productive when interacting with big data, by implementing the pandas DataFrame API on top of Apache Spark. My guess is that it has to do with the fact that the data frames are of two different lengths. I usually use df2= pd. Merging DataFrames 50 xp Merging company DataFrames 50 xp Merging on a specific column 100 xp Merging on columns with non-matching labels 100 xp Merging on multiple columns 100 xp Joining. You can think of Hadoop as a Big Data Operating System that makes it possible to run different types of workloads over all your huge datasets. Leaflet supports even more customizable markers using the awesome markers leaflet plugin. This is useful when you need to drill down to…. How to merge two dataframes with same schema This post has NOT been accepted by the mailing list yet. My goal is to easily compare the two dataframes and confirm that they both contain the same rows. Consequently, we see our original unordered output, followed by a second output with the data sorted by column z. Example - Using Formula The expression contained within the SQL SUM function does not need to be a single field. Outside Spark, the discrete tasks of selecting data, transforming that data in various ways, and analyzing the transformed results might easily require a series of separate processing frameworks, such as Apache Oozie. DataComPy's SparkCompare class will join two dataframes either on a list of join columns. Merge DataFrames. Natural join for data frames in Spark Natural join is a useful special case of the relational join operation (and is extremely common when denormalizing data pulled in from a relational database). The pandas package provides various methods for combining DataFrames including merge and concat. Union two DataFrames; Write the unioned DataFrame to a Parquet file; Read a DataFrame from the Parquet file; Explode the employees column; Flatten the fields of the employee class. Can structured data help us? We'll look at Spark SQL and its powerful optimizer which uses structure to apply impressive optimizations. merge(df1, df2, by = "row. Merging multiple data frames row-wise in PySpark. What is a Spark DataFrame? A Spark DataFrame is a distributed collection of data organized into named columns that provides operations to filter, group, or compute aggregates, and can be used with Spark SQL. One option to concatenate string columns in Spark Scala is using concat. left− Dataframe1. Type conversions in R work as you would expect. hat tip: join two spark dataframe on multiple columns (pyspark) Now assume, you want to join the two dataframe using both id columns and time columns. Trying to merge two dataframes in pandas that have mostly the same column names, but the right dataframe has some columns that the left doesn't have, and vice versa. df1 has column (A,B,C) and df2 has columns (D,C,B), then you can create a new dataframe which would be the intersection of df1 and df2 conditioned on column B and C. 0, DataSet and Dataframe API’s are becoming new standard API’s replacing RDD. right- Dataframe2. In particular, I’d like to cover the use case of when you have multiple dataframes with the same columns that you…. Pipelining is as simple as combining multiple transformations together. This finds values in column A that are equal to 1, and applies True or False to them. How to compare, match two columns from diferent dataframe and assign values from one datafram to the other. asked Jul 31 in Data Science by sourav (17. combine_first (self, other) [source] ¶ Update null elements with value in the same location in other. Binding columns (cbind) The simplest process is combining a new dataset which contains new variable(s) we wish to added to corresponding data original dataset. 0 For Python applications, you need to add this above library and its dependencies when deploying your application. merging single column from different dataframe. When testing for a non-NULL value, IS NOT NULL is the recommended comparison operator to use in SQL. any() will work for a DataFrame object to indicate if any value is missing, in some cases it may be useful to also count the number of missing values across the entire DataFrame. map(), filter(), lambda, and list comprehensions provide compact, elegant, and efficient ways to encode a few common idioms in programming. How did I do the merge? First of all, you have the zoo dataframe already, but for this exercise you will have to create a zoo_eats. Given a Pandas dataframe, we need to find the frequency counts of each item in one or more columns of this dataframe. It assumes that there are missing columns in both DataFrames but the common columns must have the same data types. python - Pandas merge two dataframes with different columns. One can perform left, right, outer or inner joins on these dataframes. While join in Apache spark is very common. Thus, if you plan to do multiple append operations, it is generally better to build a list of DataFrames and pass them all at once to the concat() function. Two DataFrames might hold different kinds of information about the same entity and linked by some common feature/column. This blog describes one of the most common variations of this scenario in which the index column is based on another column in the DDF which contains non-unique entries. Each argument can either be a Spark DataFrame or a list of Spark DataFrames. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. Merging two data. set_index('A'). merge() function. Let us now create two different DataFrames and perform the merging operations on it. Here we print the underlying schema of our DataFrame: It is important to know that Spark can create DataFrames based on any 2D-Matrix, regardless if its a DataFrame from some other framework, like Pandas, or even a plain structure. Data Type Conversion. For example, we can load a DataFrame from a. functions, which provides a lot of convenient functions to build a new Column from an old one. This is useful when you need to drill down to…. merge Merge DataFrames by indexes or columns. When performing joins in Spark, one question keeps coming up: When joining multiple dataframes, how do you prevent ambiguous column name errors? 1) Let's start off by preparing a couple of simple example dataframes // Create first example dataframe val firstDF = spark. Modules needed: import numpy as np import. Adobe Spark is an online and mobile design app. The related join() method, uses merge internally for the index-on-index (by default) and column(s)-on-index join. columns) in. two) Error: numbers of columns of arguments do not match So I created a function that can be used to combine the data from two dataframes, keeping only the columns that have the same names (I don't care about the other ones). Spark: how to handle dataframes (Part II) Python will assign automatically a dtype to the dataframe columns, we might want to combine two variables. Sometime, when the dataframes to combine do not have the same order of columns, it is better to df2. At first glance it doesn't seem that strange. Data Types¶ The modules described in this chapter provide a variety of specialized data types such as dates and times, fixed-type arrays, heap queues, double-ended queues, and enumerations. Let's discuss all different ways of selecting multiple columns in a pandas DataFrame. This reflection based approach leads to more concise code and works well when you already know the schema while writing your Spark application. Net library for Apache Spark which brings Apache Spark tools into. Spark supports below api for the same feature but this comes with a constraint that we can perform union operation on dataframes with the same number of columns. I'm surely missing something simple here. Series (['a', 'b']) >>> s2 = pd. Plus, with the evident need for handling complex analysis and munging tasks for Big Data, Python for Spark or PySpark Certification has become one of the most sought-after skills in the industry today. The related join() method, uses merge internally for the index-on-index (by default) and column(s)-on-index join. python - Pandas merge two dataframes with different columns. The apply() Family. Apply multiple aggregation operations on a single GroupBy pass. So that you can access the results, you need to alias the DataFrames to different names—otherwise you will be unable to select the columns due to name collision (see Example 4-10). Two R dataframes can be combined with respect to columns or rows. We can run the job using spark. NET for Apache Spark and ML. The related join() method, uses merge internally for the index-on-index (by default) and column(s)-on-index join. Spark: how to handle dataframes (Part II) Python will assign automatically a dtype to the dataframe columns, we might want to combine two variables. Note the $ syntax; you select columns of a data frame by using a dollar sign and the name of the column. "Since I don’t have a blog and you don’t allow anonymous comments I thought I’d shoot a quick email with a question/concern. 3, and Spark 1. 6 as a new DataFrame feature that allows users to rotate a table-valued expression by turning the unique values from one column into individual columns. I'm sure there are other fancier ways of doing this but here's how my function works. Each argument can either be a Spark DataFrame or a list of Spark DataFrames. Adding Multiple Columns to Spark DataFrames Jan 8, 2017 I have been using spark’s dataframe API for quite sometime and often I would want to add many columns to a dataframe(for ex : Creating more features from existing features for a machine learning model) and find it hard to write many withColumn statements. This reflection based approach leads to more concise code and works well when you already know the schema while writing your Spark application. The foldLeft way is quite popular (and elegant) but recently I came across an issue regarding its performance when the number of columns to add is not trivial. Merge(DataSet) Merges a specified DataSet and its schema into the current DataSet. Here is an example use of filter() function to filter out only even numbers from a list. Here we have taken the FIFA World Cup Players Dataset. When column-binding, rows are matched by position, so all data frames must have the same number of rows. Because the names of the columns that should be used when merging the data are different in both DataFrames, we need to specify the. 6 Differences Between Pandas And Spark DataFrames. 6k points) python; Big Data Hadoop & Spark (694) Data Science (984. However, not all operations on data frames will preserve duplicated column names: for example matrix-like subsetting will force column names in the result to be unique. Pandas is one of those packages and makes importing and analyzing data much easier. Spark SQL supports two different methods for converting existing RDDs into DataFrames. This is for example how we do an innerjoin. Apache Spark MLlib's DataFrame-based API provides a simple, yet flexible and elegant framework for creating end-to-end machine learning pipelines. Q&A for Work. Python also provides some built-in data types, in particular, dict, list, set and frozenset, and tuple. Learning Objectives. Recently they were introduced in Spark and made large scale data science much easier. Here I am going to show just some basic pandas stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. In [5]: df1. The "Persons" table will now look like this:. Here we have taken the FIFA World Cup Players Dataset. But due to two big. My guess is that it has to do with the fact that the data frames are of two different lengths. To create a DataFrame, you can choose to start from scratch or convert other data structures like Numpy arrays into a DataFrame. Append Spark Dataframe with a new Column by UDF To change the schema of a data frame, we can operate on its RDD, then apply a new schema. It has mutable size. PySpark provides multiple ways to combine dataframes i. [/code]The one that has usingColumns (Seq[String]) as second parameter works best, as the columns that you join on won't be duplicate. Spark's DataFrame API provides an expressive way to specify arbitrary joins, but it would be nice to have some machinery to make the simple case of. This is useful when you need to drill down to…. The "Persons" table will now look like this:. columns) in order to ensure both df have the same column order before the union. Openly pushing a pro-robot agenda. UPDATED 11/10/2018. Learning Objectives. I'm surely missing something simple here. It can be constructed from sou. How can this be achieved with DataFrames in Spark version 1. Merging strings that have identical rownames in a dataframe | Post 302882588 by Alyaa on Wednesday 8th of January 2014 03:59:23 AM The UNIX and Linux Forums Forums. How to split a text into two meaningful words in R. A community forum to discuss working with Databricks Cloud and Spark. The content of the new column is derived from the values of the existing column ; The new column is going to have just a static value (i. # When merging two data frames that do not have matching column names, we can # use the by. See also: Bar Charts¶. In order to merge the Dataframes we need to identify a column common to both of them. everyoneloves__top-leaderboard:empty,. Because if one of the columns is null, the result will be null even if one of the other columns do have information. left− Dataframe1. An introduction to interoperability of DataFrames between Scala Spark and PySpark. Join and merge pandas dataframe. 2 from Anaconda, and I’ve posted the code on GitHub. one, database. groupId = org. It assumes that there are missing columns in both DataFrames but the common columns must have the same data types. A dataframe is a two-dimensional data structure having multiple rows and columns. This article is based on O'Reilly Velocity 2019 Keynote by Lena Hall. When column-binding, rows are matched by position, so all data frames must have the same number of rows. Appending a DataFrame to another one is quite simple:. We can merge the datasets using a command of the form: m=merge(hun_2011racestats,hun_2011qualistats,by="driverNum") The by parameter identifies which column we want to merge the tables around. Spark DataFrames for large scale data science | Opensource. In Spark, data is represented by DataFrame objects, which can be thought of as a 2D structure following the tidy data format. You can merge two data frames using a column column. DataFrame can have different number rows and columns as the input. This blog provides an exploration of Spark Structured Streaming with DataFrames. Potentially, the columns are of a different type and the size of the DataFrame is mutable, and hence can be modified. DataFrame It is appeared in Spark Release 1. cacheTable("tableName") or dataFrame. The filter() function in Python takes in a function and a list as arguments. (You can report issue about the content on this page here). I have a Dataframe with strings and I want to apply zfill to strings in some of the columns. Kudu tables have a structured data model similar to tables in a traditional RDBMS. $\endgroup$ - Divyanshu Shekhar Jun 13 '18 at 7:04. When column-binding, rows are matched by position, so all data frames must have the same number of rows. 2 from Anaconda, and I’ve posted the code on GitHub. Apache Spark Tutorial Following are an overview of the concepts and examples that we shall go through in these Apache Spark Tutorials. createDataFrame takes two parameters: a list of tuples and a list of column names. It's about the impact of our work, the complexity and obstacles we face, and what is important for building better distributed systems, especially when other life-critical areas rely on and build on what we create. Uses the intersection of keys from two DataFrames. How to merge two data frames column-wise in Apache Spark 7 Answers Unable to import a datalake csv where one of the columns has the header missing and also is a multiline column. It is equivalent to a table in a relational database or a data frame in R/Python. Hi R user, I could not combine two tables. I know I can use the plyr and its friends to combine dataframes, and merge as well, but so far I don't know how to merge two dataframes with multiple columns based on 2 columns. has the notion of nullable DataFrame column schema. It assumes that if a field in df1 is missing from df2, then you add that missing field to df2 with null values. All other keyword arguments of the Matplotlib hist function can be used. In this case, we create TableA with a 'name' and 'id' column. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. Apache Spark, Spark, and the. Each argument can either be a Spark DataFrame or a list of Spark DataFrames. Add this suggestion to a batch that can be applied as a single commit. So that you can access the results, you need to alias the DataFrames to different names—otherwise you will be unable to select the columns due to name collision (see Example 4-10). Trying to merge two dataframes in pandas that have mostly the same column names, but the right dataframe has some columns that the left doesn't have, and vice versa. In this part, we're going to talk about joining and merging dataframes, as another method of combining dataframes. data analysis - useful links. head(5), or pandasDF. When row-binding, columns are matched by name, and any missing columns with be filled with NA. We can merge the datasets using a command of the form: m=merge(hun_2011racestats,hun_2011qualistats,by="driverNum") The by parameter identifies which column we want to merge the tables around. Spark's new DataFrame API is inspired by data frames in R and Python (Pandas), but designed from the ground up to support modern big data and data science applications. spark-dev mailing list archives Site index · List index. # When merging two data frames that do not have matching column names, we can # use the by. In Windows, for example, a file can be any item manipulated, edited or created by the user/OS. merge Merge DataFrames by indexes or columns. (If the two datasets have different column names, you need to set by. Not that Spark doesn't support. Much of Spark's power lies in its ability to combine very different techniques and processes into a single, coherent whole. This blog post addresses the process of merging datasets, that is, joining two datasets together based on common columns between them. 0 one could use subtract with 2 SchemRDDs to end up with only the different content from the first one. You may say that we a list or tuple of two Spark dataframes :return: single dataframe consisting of dfs' columns and data. We created two transformations. Big Data and related stuff. Plotly's team maintains the fastest growing open-source visualization libraries for R, Python, and JavaScript. rbinds a list of data frames filling missing columns with NA. Key topics covered here: • What is a merge or join of two dataframes? • What are inner, outer, left and right merges? • How do I merge two dataframes with different common column names? (left_on and right. So here we will use the substractByKey function available on javapairrdd by converting the dataframe into rdd key value pair. Question Tag: columns Filter by Select Categories Android AngularJs Apache-spark Arrays Azure Bash Bootstrap c C# c++ CSS Database Django Excel Git Hadoop HTML / CSS HTML5 Informatica iOS Java Javascript Jenkins jQuery Json knockout js Linux Meteor MongoDB Mysql node. Leveraging the power of Spark's DataFrames and SQL engine, Spark ML pipelines make it easy to link together the phases of the machine learning workflow, from data processing, to feature extraction and engineering, to model training and evaluation. What is the "Spark DataFrame". The key is the common column that the two DataFrames will be joined on. In the beginning, I ended up with googling every time I tried to combine two DataFrames. Join two data frames, select all columns from one and some columns from the other I notice that when joined dataframes have same-named column My question is. 6 as a new DataFrame feature that allows users to rotate a table-valued expression by turning the unique values from one column into individual columns. We ran micro benchmarks for three of the above examples (plus one, cumulative probability and subtract mean). Merging is a big topic, so in this part we will focus on merging dataframes using common columns as Join Key and joining using Inner Join, Right Join, Left Join and Outer Join. Merging multiple data frames row-wise in PySpark. Merging on different columns with unique values. Here I am going to show just some basic pandas stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic.