Pandas Iterate Over Series


Right? At times you may need to iterate through all rows using a for loop. Hey guysin this python pandas tutorial I have talked about how you can iterate over the columns of pandas data frame. 3 documentation Iterate over (column name, Series) pairs. Here in the third part of the Python and Pandas series, we analyze over 1. It's a great dataset for beginners learning to work with data analysis and visualization. How can I iterate over the columns of a Matlab table? The Python equivalent and assuming data was a pandas data frame would be: variable_names=data. for i, row in df. Specify the keyword argument axis='rows' to stack the Series vertically. We often want to loop over (iterate through) these values. Iterating through a range of dates in Python. Dataset used here - https://www. Example 1: Iterate through rows of Pandas DataFrame. that is as clean and easy. Then, you will print all. The Pandas Python library is an extremely powerful tool for graphing, plotting, and data analysis. Maybe PandaPy is better, but I doubt it. Here is an example that calls both the key and the value:. Dask DataFrame does not attempt to implement many Pandas features or any of the more exotic data structures like NDFrames; Operations that were slow on Pandas, like iterating through row-by-row, remain slow on Dask DataFrame; See DataFrame API documentation for a more extensive list. We're looking to protect our wealth by having diversified wealth, and, one component to this is real. Each time you iterate through it, it will yield two elements: the index of the respective row; a pandas Series with all the elements of that row; You are going to create a generator over the poker dataset, imported as poker_hands. iteritems¶ Series. Pandas is useful, especially for time-series data, but no one particularly loves it. Pandas is a data analysis library, and is better suited for working with table data in many cases, especially if you're planning to do any sort of analysis with it. Returns : tuples. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary. Welcome to Part 7 of our Data Analysis with Python and Pandas tutorial series. How can I iterate over the columns of a Matlab table? The Python equivalent and assuming data was a pandas data frame would be: variable_names=data. It yields an iterator which can can be used to iterate over all the columns of a dataframe. The keywords are the output column names 2. A simple multiprocessing wrapper. Feel free to read more about this parameter in the pandas read_csv documentation. The following are code examples for showing how to use pandas. This is useful when cleaning up data - converting formats, altering values etc. Home; Iterating through Rows. function instead of pandas. How to iterate over rows in Pandas Dataframe Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. The definition has it listed as an "Iterator over (column, series) pairs". Going deeper, our loops iterate through x, iterate through y, and adds the sum of the values to z. iteritems (self) [source] ¶ Iterator over (column name, Series) pairs. Before pandas working with time series in python was a pain for me, now it's fun. How do I iterate over a sequence in reverse order? Python 2. Concatenate the Series contained in the list units into a longer Series called quarter1 using pd. apply() function. Provided by Data Interview Questions, a mailing list for coding and data interview problems. Hello Readers, This post continues directly from exploring baby names in Part 3 of the Python and Pandas Series. How can I iterate over the columns of a Matlab table? The Python equivalent and assuming data was a pandas data frame would be: variable_names=data. I am familiar with the concept of "vectorization", and how pandas employs vectorized techniques to speed up computation. Sometimes you may want to loop/iterate over Pandas data frame and do some operation on each rows. Iterating Over Arrays¶ The iterator object nditer, introduced in NumPy 1. When passed a Series, this returns a Series (with the same index), while a list-like is converted to a DatetimeIndex:. Pandas started out in the financial world, so naturally it has strong timeseries support. This returns a numpy array containing [1953, 1954, 1955, and 1956]. I'm writing a function part of which should iterate through the rows of a Series. Here's how we would do this using the series set value method. plot() directly on the output of methods on GroupBy objects, such as sum(), size(), etc. Dataset used here - https://www. use python and pandas for. CSV or comma-delimited-values is a very popular format for storing structured data. That gets me thinking — what would be the most time-efficient way to iterate through a pandas data frame?. Example 1: Iterate through rows of Pandas DataFrame. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. Pandas has at least two options to iterate over rows of a dataframe. The best way I found is to iterate through all the records and use. You can iterate through the series with iteritems for index_val, series_val in X_test_raw. If you're brand new to Pandas, here's a few translations and key terms. DataFrame(). In addition to the above functions, pandas also provides two methods to check for missing data on Series and DataFrame objects. #native_company# #native_desc# #native_cta# Awesome Job See All Jobs Post a job for only $299. Create A pandas Column With A For Loop. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis and management using Python. iteritems [source] ¶ Lazily iterate over (index, value) tuples. Still, you don't want to get stuck. You will explore the power of Pandas DataFrames and find out about Boolean and multi-indexing with Pandas. ser_two: if 'L' not in y. Pandas' iterrows() returns an iterator containing index of each row and the data in each row as a Series. Pandas is a very versatile tool for data analysis in Python and you must definitely know how to do, at the bare minimum, simple operations on it. Iterate through Python dictionary by Keys in sorted order. In 2007, Laura Wattenburg of babynamewizard. tolist(): Word of advice, iterating over pandas objects is generally discouraged. Syntax: Series. groups dict. With the for-loop this is possible. Below is my code. MultiIndex can also be used to create DataFrames with multilevel columns. The following are code examples for showing how to use pandas. On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. It's a great dataset for beginners learning to work with data analysis and visualization. You can use. An Introduction to Pandas. Get row and column count for Pandas dataframe; Iterating over rows in Pandas dataframe; Change the order of columns in Pandas dataframe; Break a long line into multiple lines in Python; Replace all NaN values with 0's in a column of Pandas dataframe; If and else statements in Python; Create and run a function in Python. Convert a pandas dataframe in a numpy array, store data in a. apply() function. randint(0, 10, 4)) ser. To my surprise I produced 3 labels but only had data in 2 groups. Series object -- basically the whole column for my purpose today. On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. zip lets you iterate over the lists in a similar way, but only up to the number of elements of the smallest list. Pandas started out in the financial world, so naturally it has strong timeseries support. Iterate through a group. But if you find yourself iterating through a series, you should question whether you're doing things in the best possible way. If instead of a Series, we just wanted an array of the numbers that are in the 'summitted' column, then we add '. In a Python Pandas DataFrame, I'm trying to apply a specific label to a row if a 'Search terms' column contains any possible strings from a joined, pipe-delimited list. In this tutorial, we'll go over setting up a large data set to work with, the groupby() and pivot_table() functions of pandas, and finally how to visualize data. for this I need to iterate each list items and update. Create an example dataframe. 0 introduced list comprehensions, with a syntax that some found a bit strange: [(x,y) for x in a for y in b] This iterates over list b for every element in a. You can use apply on groupby objects to apply a function over every group in Pandas instead of iterating over them individually in Python. To iterate through an entire DataFrame, you need to use the iterrows() function. Iterating Over Arrays¶ The iterator object nditer, introduced in NumPy 1. Apply a function to every row in a pandas dataframe. ' ----- ' Purpose: Loop through all embedded charts on all sheets in a workbook ' ----- Sub loopChartsAllSheet() Dim sh As Worksheet Dim chs As ChartObject 'Iterate through all the sheets in the workbook 'Important: use Worksheets and not Sheets 'Sheets can contain Chart or Worksheet (using. Here is an example that calls both the key and the value:. Here, the column means the column heading, title, label, etc, and the series is a pandas. You can go to my GitHub-page to get a Jupyter notebook with all the above code and some output: Jupyter notebook. zip lets you iterate over the lists in a similar way, but only up to the number of elements of the smallest list. Series = Single column of data. Pandas is a very versatile tool for data analysis in Python and you must definitely know how to do, at the bare minimum, simple operations on it. Create A pandas Column With A For Loop. How to fix 'pandas. Iterating through tuples is done in the same format as iterating through lists or strings above. Now, I do understand that this behavior comes from the fact, that the groups with a nan in the group name are ignored in the loop but they are present in the grouped. com discovered a peculiar trend in baby names, specifically the last letters in the names of newborns. name: str or None, default "Pandas" The name of the returned namedtuples or None to return regular tuples. pandas: applying a function successively over rows * series of exponetially increasing values * (it will still be faster than iterating manually. A quick aside here. The best way I found is to iterate through all the records and use. Now we've seen how to access values in the DataFrame. But when I have to create it from multiple columns and those cell values are not unique to a particular column then do I need to loop your code again for all those columns? If that is the case then how repetition of values will be taken care of? Otherwise it will over write the previous dummy column created with the same name. import pandas as pd from pandas import DataFrame, Series Note: these are the recommended import aliases The conceptual model DataFrame object: The pandas DataFrame is a two-dimensional table of data with column and row indexes. Series( data, index, dtype, copy) The parameters of the constructor are as follows −. Iterating in Python is slow, iterating in C is fast. Pandas Series example DataFrame: a pandas DataFrame is a two (or more) dimensional data structure - basically a table with rows and columns. Apply a function to every row in a pandas dataframe. You can achieve the same results by using either lambada, or just sticking with pandas. Whats people lookup in this blog:. Series object: an ordered, one-dimensional array of data with an index. A quick aside here. You will explore the power of Pandas DataFrames and find out about Boolean and multi-indexing with Pandas. backup restore sql database through AttachDBFilename. common' has no attribute. Here, the column means the column heading, title, label, etc, and the series is a pandas. For instance, one common problem we face is the incorrect treatment of variables in Python. In addition to the performance boost noted above for both the ndarray and the Series, vectorized code is often more readable. Join Jonathan Fernandes for an in-depth discussion in this video, Iterate through a group, part of pandas Essential Training. iteritems() function iterates over the given series object. It is usually best to avoid iterating. Draw simple lines in Inkscape What is the white spray-pattern residue inside these Falcon Heavy nozzles? Can an x86 CPU running in real. Still, you don’t want to get stuck. If you're brand new to Pandas, here's a few translations and key terms. Iterating Over Arrays¶ The iterator object nditer, introduced in NumPy 1. Then, you will print all. The pandas package offers spreadsheet functionality, but because you're working with Python, it is much faster and more efficient than a traditional graphical spreadsheet program. In a Python Pandas DataFrame, I'm trying to apply a specific label to a row if a 'Search terms' column contains any possible strings from a joined, pipe-delimited list. If this is a database records, and you are iterating one record at a time, that is a bottle neck, though not very big one. Let's take a quick look at pandas. PS:-column=0 is an object datatype. Returns : tuples. In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. As an example, consider time series over. Because Python is a high-level, interpreted language, it doesn't have fine grained-control over how values in memory are stored. To iterate through DataFrame’s row in pandas way one can use: Because iterrows returns a Series for. In this Pandas Tutorial, we learned how to add a new column to Pandas DataFrame with the help of detailed Python examples. Apply a function to every row in a pandas dataframe. How to iterate through a sorted dataframe in pandas? I've been looking around online and cant find anything. Delete column from pandas DataFrame using del df. pandas documentation: MultiIndex Columns. You will explore the power of Pandas DataFrames and find out about Boolean and multi-indexing with Pandas. groupby(''). In this post we will take a deep dive into dictionaries and ways to iterate over dictionary and find out how to sort a dictionary. plot() directly on the output of methods on GroupBy objects, such as sum(), size(), etc. Pandas is useful, especially for time-series data, but no one particularly loves it. What is pandas? (Introduction to the Q&A series) (6:24) pandas is a full-featured Python library for data analysis, manipulation, and visualization. size() command and get both the group name and count. it to iterate through the portions of the data set corresponding. As a rule of thumb, if you calculate more than one column of results, your result will be a Dataframe. 3 documentation Iterate over (column name, Series) pairs. In Python we find lists, strings, ranges of numbers. When passed a Series, this returns a Series (with the same index), while a list-like is converted to a DatetimeIndex:. When the user enters the sentinel value, then output the contents of the data structure. The Pandas Python library is an extremely powerful tool for graphing, plotting, and data analysis. Now we’ve seen how to access values in the DataFrame. An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values. else: row['ifor'] = y. Returns : tuples. Right? At times you may need to iterate through all rows using a for loop. Iteration is a general term for taking each item of something, one after another. Good options exist for numeric data but text is a pain. As an example, consider time series over. To my surprise I produced 3 labels but only had data in 2 groups. 20 Dec 2017. MultiIndex can also be used to create DataFrames with multilevel columns. This is part 2 of a four-part series on how to select subsets of data from a pandas DataFrame or Series. Join Jonathan Fernandes for an in-depth discussion in this video, Iterate through a group, part of pandas Essential Training. Series( data, index, dtype, copy) The parameters of the constructor are as follows −. 6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. If this is a database records, and you are iterating one record at a time, that is a bottle neck, though not very big one. Python dictionary type provides iterator interface where it can be consumed by for loops. frame I need to read and write Pandas DataFrames to disk. I feel like I am constantly looking it up, so now it is documented: If you want to do a row sum in pandas, given the dataframe df:. Specify the keyword argument axis='rows' to stack the Series vertically. Borrowed value does not live long enough when iterating over a generic value with a lifetime on the. Sheets leads to Type mismatch) For Each sh In. Pandas is really badly designed, in the same way that most Python libraries are: each function has so many parameters. iloc[, ], which is sure to be a source of confusion for R users. Some of Pandas reshaping capabilities do not readily exist in other environments (e. A python Dictionary is one of the important data structure which is extensively used in data science and elsewhere when you want to store the data as a key-value pair. In a Python Pandas DataFrame, I'm trying to apply a specific label to a row if a 'Search terms' column contains any possible strings from a joined, pipe-delimited list. Because Python is a high-level, interpreted language, it doesn't have fine grained-control over how values in memory are stored. for x in df_one. Use a for loop to iterate over [jan, feb, mar]: In each iteration of the loop, append the 'Units' column of each DataFrame to units. Create A pandas Column With A For Loop. Here are a couple of examples to help you quickly get productive using Pandas' main data structure: the DataFrame. …As we mentioned earlier,…each of these groups are data frames. It makes analysis and visualisation of 1D data, especially time series, MUCH faster. Iterate through a group. Let's consider that we're multi-billionaires, or multi-millionaires, but it's more fun to be billionaires, and we're trying to diversify our portfolio as much as possible. Let's examine a few of the common techniques. …As an example, on the olympics dataset we are working on,…if we group by each olympic here,…then the key would be the olympic edition or year,…and the group portion would be. Alternative: select the Series df['Weekday'] first:. DataFrames are column based, so you can have a single DataFrame with multiple dtypes. 6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. …As we mentioned earlier,…each of these groups are data frames. This generally happens when:. Example #1: Use Series. It yields an iterator which can can be used to iterate over all the columns of a dataframe. Given a Data Frame, we may not be interested in the entire dataset but only in specific rows. To iterate through an entire DataFrame, you need to use the iterrows() function. I feel like I am constantly looking it up, so now it is documented: If you want to do a row sum in pandas, given the dataframe df:. How to use the pandas module to iterate each rows in Python. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python's built-in functions. In short, basic iteration (for i in object. Both disk bandwidth and serialization speed limit storage performance. Let's examine a few of the common techniques. The types are being converted in your second method because that's how numpy arrays (which is what df. Draw simple lines in Inkscape What is the white spray-pattern residue inside these Falcon Heavy nozzles? Can an x86 CPU running in real. Other data structures, like DataFrame and Panel, follow the dict-like convention of iterating over the keys of the objects. iterrows(): # do something with row [/code]The key in this. Now we’ve seen how to access values in the DataFrame. Still, you don’t want to get stuck. Pandas objects (Index, Series, DataFrame) can be thought of as containers for arrays, which hold the actual data and do the actual computation. Iterate through a DataFrame with a lot of elements is not very helpful in many cases. See the Package overview for more detail about what's in the library. We just need to provide the dictionary in for loop. This chapter presents different ways of iterating through a Pandas DataFrame and why vectorization is the most efficient way to achieve it. If so, I'll show you two different methods to create pandas DataFrame: By typing the values in Python itself to create the DataFrame; By importing the values from a file (such as an Excel file), and then creating the DataFrame in Python based on the values imported; Method 1: typing values in Python to create pandas DataFrame. A Series is a one-dimensional array-like object containing any NumPy data type as values as well as data labels called the index. In This tutorial we will learn how to access the elements of a series in python pandas. pandas is an open-source library that provides high-performance, easy-to-use data structures, and data analysis tools for Python. Syntax: Series. iloc[, ], which is sure to be a source of confusion for R users. common' has no attribute. From a dictionary of one-dimensional structures, such as one-dimensional NumPy arrays, lists, dicts, or pandas Series. ser_one: for y in df_two: # iterate through the rows so you get both columns if 'MBTS' not in y. But there's a lot more to for loops than looping through lists, and in real-world data science work, you may want to use for loops with other data structures, including numpy arrays and pandas DataFrames. In this example, we will create a dataframe with four rows and iterate through them using iterrows. However, pandas and 3rd party libraries may extend NumPy's type system to add support for custom arrays (see dtypes). As an example if I have: foo -1 7 0 85 1 14 2 5 how can I loop over them so the that each iteration I would have -1 & 7, 0 & 85, 1 & 14 and 2 & 5 in variables?. Before Pandas, I would initialize an empty list to hold the one or more IPs and then I would iterate through the data structure (strained in this example) and where the interface “column” value (which in this list of lists in the strained variable is at index 5) was equal to ‘Vlan1’ I appended that IP to the list. it to iterate through the portions of the data set corresponding. Pandas Series example DataFrame: a pandas DataFrame is a two (or more) dimensional data structure - basically a table with rows and columns. This is a common question I see on the forum and I thought I make a short video demonstrate how to do that. You will perform statistical, time series computations, and implement them in financial and scientific applications. As an example if I have: foo -1 7 0 85 1 14 2 5 how can I loop over them so the that each iteration I would have -1 & 7, 0 & 85, 1 & 14 and 2 & 5 in variables?. Let's examine a few of the common techniques. Preliminaries. type and x in y. The columns are made up of pandas Series objects. Enter search terms or a module, class or function name. Here in the third part of the Python and Pandas series, we analyze over 1. Sometimes you may want to loop/iterate over Pandas data frame and do some operation on each rows. 6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. How to use the pandas module to iterate each rows in Python. 6 million baby name records from the United States Social Security Administration from 1880 to 2010. Loop through Row Data Option 1. for index, row in df. Vectorized functions broadcast operations over the entire series or DataFrame to achieve speedups much greater than conventionally iterating over the data. Home; Iterating through Rows. You should not use any function with "iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting. iterrows(): # do some logic here Or, if you want it faster use itertuples() But, unutbu's suggestion to use numpy functions to avoid iterating over rows will produce the fastest code. They are from open source Python projects. 6, provides many flexible ways to visit all the elements of one or more arrays in a systematic fashion. The method read_excel() reads the data into a Pandas Data Frame, where the first parameter is the filename and the second parameter is the sheet. In this way, you can think of a Pandas Series a bit like a specialization of a Python dictionary. Equity Ranking Backtest with Python/Pandas and iterate through month by month, selecting the symbols that meet our ranking criteria. At the end, it boils down to working with the method that is best suited to your needs. If instead of a Series, we just wanted an array of the numbers that are in the 'summitted' column, then we add '. it to iterate through the portions of the data set corresponding. ser_one: for y in df_two: # iterate through the rows so you get both columns if 'MBTS' not in y. Most of the time, these functions suffice what you need to achieve. As an example, consider time series over. Removing rows by the row index 2. plot() directly on the output of methods on GroupBy objects, such as sum(), size(), etc. First we will use Pandas iterrows function to iterate over rows of a […] DA: 20 PA: 26 MOZ Rank: 11. Looping using the. replace(year=x. How can I do conditional if, elif, else statements with Pan. import pandas as pd from pandas import DataFrame, Series Note: these are the recommended import aliases The conceptual model DataFrame object: The pandas DataFrame is a two-dimensional table of data with column and row indexes. #native_company# #native_desc# #native_cta# Awesome Job See All Jobs Post a job for only $299. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the inner loop in Cython. Use the Series() class method of the Pandas Library and pass a Python list to it in order to have an object returned. I have a sorted dataframe but when I try iterrows() it automatically goes back to iterating based on the index number. Pandas does support iterating through a series much like a dictionary, allowing you to unpack values easily. This returns a numpy array containing [1953, 1954, 1955, and 1956]. …As an example, on the olympics dataset we are working on,…if we group by each olympic here,…then the key would be the olympic edition or year,…and the group portion would be. The years are shifted in the past, so that I have to add a constant number of years to every element of that column. Please find …. The second half will discuss modelling time series data with statsmodels. 1 at the moment): iterrows: dtype might not match from row to row. This is a common beginner construct. Let's take a quick look at pandas. In this loop article let's see how you can loop through chart series. 39 Responses to "Python: iterate (and read) all files in a directory (folder)" Dt Says: December 23rd, 2008 at 11:38. A pandas Series can be created using the following constructor − pandas. I want to separate this column into three new columns, 'City, 'State' and 'Country'. As per the Pandas Documentation,To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy. Here's how we would do this using the series set value method. In 2007, Laura Wattenburg of babynamewizard. replace(year=x. The types are being converted in your second method because that's how numpy arrays (which is what df. From a dictionary of one-dimensional structures, such as one-dimensional NumPy arrays, lists, dicts, or pandas Series. In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. Sometimes you may want to loop/iterate over Pandas data frame and do some operation on each rows. Provided by Data Interview Questions, a mailing list for coding and data interview problems. What the tutorial will teach students. In Python we find lists, strings, ranges of numbers. On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. For Loops and Iterations A For Loop is a method of iterating through a string, list, dictionary, data frame, series, or anything else that you would like to iterate through. Series object -- basically the whole column for my purpose today. It's similar in structure, too, making it possible to use similar operations such as aggregation, filtering, and pivoting. Pandas has at least two options to iterate over rows of a dataframe. Most pandas users quickly get familiar with ingesting spreadsheets, CSVs and SQL data. Equity Ranking Backtest with Python/Pandas and iterate through month by month, selecting the symbols that meet our ranking criteria. com discovered a peculiar trend in baby names, specifically the last letters in the names of newborns. >NOTE: A basic Pandas Series object is a NumPy array that is one. I have list with 10000 record and want to update field "Business" whose value is AAA to BBB. To iterate means to go through an item that makes up a variable. concat takes a list of Series or DataFrames and returns a Series or DataFrame of the concatenated objects. Here are a couple of examples to help you quickly get productive using Pandas' main data structure: the DataFrame. Both disk bandwidth and serialization speed limit storage performance. Most of the time that's through stackoverflow but here's one that deals with parallelization and efficiency that I thought would be helpful. Thus we have a quadratic scan when a linear one would suffice. The columns are made up of pandas Series objects. Provided by Data Interview Questions, a mailing list for coding and data interview problems. iterrows(): print(row['column']) however, I suggest solving the problem differently if performance is of any concern. The behavior of basic iteration over Pandas objects depends on the type. "iloc" in pandas is used to select rows and columns by number, in the order that they appear in the data frame. Looping using the. x = [2, 3, 4] y =. In this article we will read excel files using Pandas. SQL or bare bone R) and can be tricky for a beginner. plot() directly on the output of methods on GroupBy objects, such as sum(), size(), etc. Series with the Series() method. This page introduces some basic ways to use the object for computations on arrays in Python, then concludes with how one can accelerate the inner loop in Cython. How to delete DataFrame columns by name or index in Pandas? DataFrame slicing using loc in Pandas; How to use Stacking using non-hierarchical indexes in Pandas? Remove duplicate rows from Pandas DataFrame where only some columns have the same value; How to get a value from a cell of a DataFrame? How to create and print DataFrame in pandas?. iteritems¶ Series. The post Six ways to reverse pandas dataframe appeared first on Erik Marsja. Series = Single column of data. Good options exist for numeric data but text is a pain. You should never modify something you are iterating. These methods evaluate each object in the Series or DataFrame and provide a boolean value indicating if the data is missing or not. However, pandas and 3rd party libraries may extend NumPy's type system to add support for custom arrays (see dtypes).