# Create a list to store the data grades = [] # For each row in the column, for row in df['test_score']: # if more than a value, if row > 95: # Append a letter grade grades. Adding a new column by passing as Series: one two three a 1. This is much better than the basic looping because the object passed to the function is Pandas series object with index as rows (axis=0) or Dataframe column (axis=1) and it returns a new Series or DataFrame object. x = our loops iterate through x, iterate through y, and. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary. The Pandas Series, Species_name_blast_hit is an iterable object, just like a list. The procedural way of doing this would be to iterate through all of the items in the series and increase the values directly. python - Pandas: for loop through columns - Stack Overflow https://stackoverflow. Fortunately, pandas has a dummies. The following are code examples for showing how to use pandas. result is a SQLAlchemy ResultProxy object that allows you to iterate over the results of the statement you executed. Maria Lobillo Santos. Find the columns with numeric values, but stored as string. Summing over several million rows is nothing to worry about unless you’re doing it in a hot loop. iterrows Iterate over DataFrame rows as (index, Series) pairs. This is a common question I see on the forum and I thought I make a short video demonstrate how to do that. Selecting Subsets of Data in Pandas: Part 2. Syntax DataFrame_name. When sort = True is passed to groupby (which is by default) the groups will be in sorted order. Series object -- basically the whole column for my purpose today. The result returned is the same format. iterate over pandas dataframe and update the value - AttributeError: can't set attribute I am trying to iterate over a pandas dataframe and update the value if. Loop through gridview using jQuery. This method returns an iterable tuple (index, value). Pandas does support iterating through a series much like a dictionary, allowing you to unpack values easily. This is not guaranteed to work in all cases. Panel − item labels. $\begingroup$ Yes, iterate through the columns of dataframe. sum(X,axis=0). The behavior of basic iteration over Pandas objects depends on the type. Python Pandas : Replace or change Column & Row index… Pandas : Loop or Iterate over all or certain columns… Pandas : count rows in a dataframe | all or those… Python Pandas : Select Rows in DataFrame by… Pandas : Get unique values in columns of a Dataframe… Pandas : 4 Ways to check if a DataFrame is empty in Python; Pandas : 6. Iterate over rows using. Ultimately, I'd like a one-liner to plot all bar charts of all categorical variables. The first idea I had was to create the collection of data frames shown below, then loop through the original data set and append in new values based on criteria. pandas is an open-source library that provides high-performance, easy-to-use data structures, and data analysis tools for Python. The for loop is a great tool to iterate (or go through) each value within a list. Iterating over column values can be inefficient if we utilize the pandas iterators. I have two answers for you. Iterate over rows and columns pandas DataFrame Pandas find row where values for column is. This method returns an iterable tuple (index, value). Now, if I do a type here, we can see that 00. Vba to loop through column keyword after analyzing the system lists the list of keywords related and the list of websites with related content, in addition you can see which keywords most interested customers on the this website. In total, I compared 8 methods to generate a new column of values based on an existing column (requires a single iteration on the entire column/array of values). iterrows() You can iterate over rows with the iterrows() function, like this: [code]for key, row in df. itertuples() >>> import pandas as pd >>> data = [{'a': 2, 'b': 3, 'c': 4}, {'a': 5, 'b': 6, 'c': 7}, {'a': 8, 'b. Introduction In this tutorial we will discuss about looping through the rows and columns of a gridview using jQuery. Iterating in Python is slow, iterating in C is fast. Now even if you slice the str columns away, the resulting array will still consist of object dtype and might not play well with other libraries such as scikit-learn which are expecting a. Iterate an operations over groups group by the data types of the columns (i. Now I want to iterate over the rows of the above frame. In short, basic iteration (for i in object) produces − Series − values. The procedural way of doing this would be to iterate through all of the items in the series and increase the values directly. We will create a new DataFrame object and then iterate through the data within the DataFrame table, finally , we will sum up those negative values then print out their average. apply() 100 xp Vectorization over pandas series 50 xp Why pandas vectorization is so fast? 50 xp pandas vectorization in action 100 xp Vectorization with NumPy arrays using. Iterating With Python Lambdas iterate over x, multiply the odd values by 5 and add them to the list y. It’s much better to extract the underlying NumPy arrays and work with those. Pandas use rank method to find the ranking of elements in a DataFrame; How to get Length Size and Shape of a Series in Pandas? If value in row in DataFrame contains string create another column equal to string in Pandas; How to measure Variance and Standard Deviation for DataFrame columns in Pandas? What is difference between iloc and loc in. Iterate through a DataFrame with a lot of elements is not very helpful in many cases. Let us see examples of how to loop through Pandas data frame. Note that when working with the plural methods such as rows() and columns() you may wish to use the rows(). learnpython) submitted 2 years ago by Exo-Genesis Hey everyone, complete newbie to Python (and programming) here!. name: str or None, default “Pandas” The name of the returned namedtuples or None to return regular tuples. Iterate over string with index using range() range(len (stringObj) ) function will generate the sequence from 0 to n -1 ( n is size of string). I'm working with a small part of your. The Pandas API is very large. Selecting Subsets of Data in Pandas: Part 2. In this case, you must also tell pandas. iterrows(): # do something with row [/code]The key in this. Keys and values are iterated over in an arbitrary order which is non-random, varies across Python implementations, and depends on the dictionary’s history of insertions and deletions. Let’s select the same columns but. A step-by-step Python code example that shows how to Iterate over rows in a DataFrame in Pandas. from a 2D NumPy's ndarray. ['target_value'] # Iterate through columns, dispatching and transforming each feature. This is not guaranteed to work in all cases. ForEach() actually seems quicker than using either ForEach or For to iterate over the collection. This is a common question I see on the forum and I thought I make a short video demonstrate how to do that. Using Pandas to create a conditional column by selecting multiple columns in two different dataframes. How to iterate over column of a Pandas Dataframe. value – int, long, float, string, or dict. There's a number of different approaches, but here's the way that I'm going to work through this one. sum(X,axis=1) and column sums: import numpy as np np. Series: a pandas Series is a one dimensional data structure (“a one dimensional ndarray”) that can store values — and for every value it holds a unique index, too. Fortunately, pandas has a dummies. extract The pattern is a regular expression (regex). So, for example, I would like to have something like that: for row in df. Documentation: pandas. Python Pandas Dataframe Conditional If, Elif, Else 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. So this article is a part show-and-tell, part quick tutorial on the new features. All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. Once you have a tuple representing one row or column, you can loop through its Cell objects and print their values. We typically want to extract this list of uniques from a column in a table/range that contains duplicate values. Here is how to iterate over two lists and their indices using enumerate together with zip: alist = ['a1', 'a2', 'a3'] blist = ['b1', 'b2', 'b3'] for i, (a, b) in enumerate(zip(alist, blist)): print i, a, b. The mapper () function takes a minimum of two parameters: first the data class to modify, and then the table object onto which that data class should be mapped. I have done a short test to see which one of the three is the least time consuming. rows: print row['c1'], row['c2'] Is it possible to do that in pandas? I found similar question. apply to send a column of every row to a function. We can use the pandas. When your application calls engine. com But it comes in handy when you want to iterate over columns of your import pandas as. Let's use this on the Planets data, for now dropping rows with missing values:. And iterating through the columns of the DataFrame thus results in more readable code: for col in df. Pandas’ iterrows() returns an iterator containing index of each row and the data in each row as a Series. import pandas as pd import numpy as np def impute_with_median (df): """Iterate through columns of Pandas DataFrame. Iterate over dataframe groups; Group by and change aggregation column name; Get group by key; List values for each group; Custom aggregation; For Dataframe usage examples not related to GroupBy, see Pandas Dataframe by Example. I want to iterate through one of the columns within each of the worksheets and add the rows to a new list (or column). JQuery has given a function each() from which we can iterate through any collection, but in this tutorial we will use this function to iterate through the gridview. In addition to above points, Pandas and Pyspark DataFrame have some basic differences like columns selection, filtering, adding the columns, etc. columns: series = df[col] # do something with series. All metrics functions have the same interface. Python Pandas : Count NaN or missing values in… Python Pandas : How to Drop rows in DataFrame by… Pandas : Loop or Iterate over all or certain columns… Python Pandas : Drop columns in DataFrame by label… Pandas : How to create an empty DataFrame and append… Python Pandas : Replace or change Column & Row index… Pandas : How to Merge. row(key)`` and ``Frame. iterrows Iterate over DataFrame rows as (index, Series) pairs. DA: 79 PA: 8 MOZ Rank: 74. "iloc" in pandas is used to select rows and columns by number, in the order that they appear in the data frame. value – int, long, float, string, or dict. Elegant way to get all categorical columns in pandas? Stackoverflow. Yes you can, and there are two ways you could do this (and an alternative down at the bottom). The last datatypes of each column, but not necessarily in the corresponding order to the listed columns. How can I iterate through a column of a pandas DataFrame and return value from another column? Ask Question. A SQLAlchemy engine works with a pool of connection. Iterating through columns and rows in NumPy and Pandas Michael Allen NumPy and Pandas April 10, 2018 October 3, 2018 1 Minute Using apply_along_axis (NumPy) or apply (Pandas) is a more Pythonic way of iterating through data in NumPy and Pandas (see related tutorial here ). Creating new columns by iterating over rows in pandas dataframe a pandas dataframe column. 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. Calculate percentage of NaN values in a Pandas Dataframe for each column. which I am not covering here. They are − Splitting the Object. Good to know before start. To create pandas DataFrame in Python, you can follow this generic template:. Notes Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). [code]columns = list(df. Applying a function. iteritems(): Find index label for min/max values in column. Download with Google Download with Facebook or download with email. Right? At times you may need to iterate through all rows using a for loop. com The behavior of basic iteration over Pandas objects depends on the type. Mapping your objects to SQL rows. rows: print row['c1'], row['c2'] Is it possible to do that in pandas? I found similar question. It is useful for verbose console output when creating different command line tools that process data:. search(pattern, string, flags=0). apply ( calculate_taxes ). sum() on 50 million rows, it takes around 65 milliseconds on my ~2015 macbook. Select/Fetch Records with Column Names. With files this large, reading the data into pandas directly can be difficult (or impossible) due to memory constrictions, especially if you’re working on a prosumer computer. Select Rows by index value; Select rows by column value; Select rows by multiple column values; Select columns starting with; Select all columns but one; Apply an aggregate function to every column; Apply an aggregate function to every row; Transform dataframe; Shuffle rows in DataFrame; Iterate over all rows in a DataFrame; Randomly sample. col(key)`` - return a ``pandas. But how would you do that? To accomplish this task, you can use tolist as follows: df. I have constructed a pandas data frame in sorted order and would like to iterate over groups having identical values of a particular column. The pandas DataFrame has an attribute that can aid with this as well:. For example, you can use. And iterating through the columns of the DataFrame thus results in more readable code: for col in df. 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. Using the Pandas library from Python, this is made an easy task. As we iterate through the labels, we set the value of the row and column to a string so we can do a string comparison to Kevin Durant. In python, iterating over the rows is going to be (a lot) slower than doing vectorized operations. I want to iterate through one of the columns within each of the worksheets and add the rows to a new list (or column). A good example is getting from the values in our What is your gender? column to numeric values. and then iterate over the items:. $\begingroup$ I think the the question is about comparing the values in two different columns in different dataframes as question person wants to check if a person in one data frame is in another one. One strength of DataFrames from my perspective are the filtering capabilities. Apply Operations To Groups In Pandas. You should never modify something you are iterating over. columns: series = df[col] # do something with series. 5, but this same approach should work with Python 2. The procedural way of doing this would be to iterate through all of the items in the series and increase the values directly. The text column has repeated values (1000 unique values, each repeated 1000 times) while the numeric column is all unique. Vba to loop through column keyword after analyzing the system lists the list of keywords related and the list of websites with related content, in addition you can see which keywords most interested customers on the this website. One common task we do as data analysts is creating a list of unique values from a column in a data set. If the value in the column is in the top n values then keep that value, otherwise bucket in "other". When your application calls engine. Read the rest of the lines one by one. If your CSV files doesn’t have column names in the first line, you can use the names optional parameter to provide a list of column names. every() and cells(). Re: VBA looping through cells in a column to check for a match It's not clear what you want to do if a match is found nor how you pass the ID number you want to search for into a variable in VBA. If I want to perform an operation on each column of a pandas dataframe, is it okay to iterate over the dataframe columns using a for loop? By doing something like so: for label in df_index_list: function(df[label]) I ask because I have read a lot about how iterating over dataframes is very inefficient and wellnot using the dataframes right. Pandas: Faster way to iterate through a dataframe and add new data based on operations I want to pandas to look into values in 2 columns in each row of df1, look for the match in another df2, and paste this in a new column in df1 in the same row and continue. the locations of peaks and troughs). iterrows(): Iterate over the rows of a DataFrame as (index, Series) pairs itertuples(): Iterate over the rows of a DataFrame as tuples of the values. Like what has been mentioned before, pandas object is most efficient when process the whole array at once. You should test this on a blank sheet that only contains 7-digit values from A4 down (number of rows doesn't matter, i. I am initializing a DataFrame with 0 and then update it by iteratively indexing into indvidual columns. A step-by-step Python code example that shows how to Iterate over rows in a DataFrame in Pandas. So, for example, I would like to have something like that: for row in df. Get the unique values (rows) of the dataframe in python pandas. Now iterate over this sequence and for each index access the character from string using operator [] i. The code below generates a figure with three subplots displayed vertically, each of which shows a bar plot for a particular column of the data frame. com Is there a way to get all categorical variables in Pandas? The best way I know is to iterate through all columns and check whether the dtype is categorical. 1 day ago · I want to iterate through one of the columns within each of the worksheets and add the rows to a new list (or column). ForEach() can access the backing array of list directly without having to go through the indexer, or use an enumerator. The last datatypes of each column, but not necessarily in the corresponding order to the listed columns. Pandas Series example DataFrame: a pandas DataFrame is a two (or more) dimensional data structure – basically a table with rows and columns. The Pandas Series, Species_name_blast_hit is an iterable object, just like a list. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. com/channel/UC2_-PivrHmBdspaR0klVk9g?sub_c. $\endgroup$ – Harshith Jul 17 at 6:33. Iterate over rows and columns pandas DataFrame Pandas find row where values for column is. iterrows`: Iterate over the rows of a DataFrame as (index, Series) pairs. How would you do it? pandas makes it easy, but the notation can be confusing and thus difficult. The easiest way I have found is to use [code ]pandas. apply(): Apply. For every row I want to be able to access its elements (values in cells) by the name of the columns. Iterate through a DataFrame with a lot of elements is not very helpful in many cases. Broadcasting Iteration. Calculate percentage of NaN values in a Pandas Dataframe for each column. I am a data scientist with a decade of experience applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts -- from election monitoring to disaster relief. First we will use Pandas iterrows function to iterate over rows of a. we will want to test equality in a single column to multiple values. DOC: improve docs on iteration Iterating through pandas Iterate over the rows of a DataFrame as tuples of the values. First, let's create a simple dataframe with nba. rows: print row['c1'], row['c2'] Is it possible to do that in pandas? I found similar question. keywords are the column names. set_option. Writing this, I realize that I can iterate over the columns. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Background: Each of the worksheets represents a different community of farmers and each column of each worksheet is a piece of demographic data. The first two are ways to apply column-wise functions on a dataframe column: use_column: use pandas column. apply(): Apply. Using pandas DataFrames to process data from multiple replicate runs in Python Posted on June 26, 2012 by Randy Olson Posted in python , statistics , tutorial Per a recommendation in my previous blog post , I decided to follow up and write a short how-to on how to use pandas to process data from multiple replicate runs in Python. iteritems (self) [source] ¶ Lazily iterate over (index, value) tuples. But how would you do that? To accomplish this task, you can use tolist as follows: df. Optimum approach for iterating over a DataFrame examine different ways in which we can replace null values in a column. The SAS specification is not public and had to be reverse engineered through examples. This might sound a little complicated, but it can significantly simplify your code!. Apply the capitalizer function over the column ‘name’ Map the capitalizer lambda function over each element in. You should never modify something you are iterating over. The types are being converted in your second method because that's how numpy arrays (which is what df. You have reached the end of our Python dictionary tutorial! Complete your learning by taking DataCamp's the free Intro to Python for Data Science course to learn more about the Python basics that you need to know to do data science and the Intermediate Python for Data Science course to learn more about the control flow. Dropping rows and columns in pandas Create a new dataframe called df that includes all rows where the value of a cell in the name column does not equal "Tina". Performing column level analysis is easy in pandas. Creating a Pandas' DataFrame 1: From columns of Series, packed in a dict with Column Names. apply ( calculate_taxes ). I want to do a find and replace between 2 dataframes. It seems to be a bug so I am posting here as well. iteritems¶ DataFrame. value – int, long, float, string, or dict. In R, you can use the apply() function to apply a function over every row or column of a matrix or data frame. When we convert a column to the category dtype, pandas uses the most space efficient int subtype that can represent all of the unique values in a column. How to get the maximum value of a specific column in python pandas using max() function. # Value of 1st row and 1st column sheet. $\begingroup$ Yes, iterate through the columns of dataframe. How to Iterate Through Rows with Pandas iterrows() Pandas has iterrows() function that will help you loop through each row of a dataframe. With this convention, the current value of a variable is the last value recorded in a previous line in the table. net4 & C#. The SAS specification is not public and had to be reverse engineered through examples. I could iterate through the list, but I suspect there is a faster way to do it with pandas. It’s much better to extract the underlying NumPy arrays and work with those. Pandas : Loop or Iterate over all or certain columns… Python Pandas : How to create DataFrame from dictionary ? Python Pandas : How to add rows in a DataFrame using… Pandas : Get unique values in columns of a Dataframe… Pandas : Drop rows from a dataframe with missing… Python Pandas : How to drop rows in DataFrame by…. So, for example, I would like to have something like that: for row in df. This is probably one of the most common uses for the for. I'm working with a small part of your. Just about every Pandas beginner I've ever worked with (including yours truly) has, at some point, attempted to apply a custom function by looping over DataFrame rows one at a time. For each column in the Dataframe it returns an iterator to the tuple containing the column name and column contents as series. The essential difference is the presence of the index: while the Numpy Array has an implicitly defined integer index used to access the values, the Pandas Series has an explicitly defined index associated with the values. If this condition does not hold true, I want to print "Condition ends at 2017-01-03. import pandas as pd import numpy as np def impute_with_median (df): """Iterate through columns of Pandas DataFrame. There are 131 rows, one for each year and 6,865 columns, or names. The types are being converted in your second method because that's how numpy arrays (which is what df. Iterate over rows and columns in Pandas DataFrame Python Programming. Ways to iterate over rows. Remove any empty values. In R, you can use the apply() function to apply a function over every row or column of a matrix or data frame. All none values are dropped and if there is no match the dataframe becomes empty. Iterate over rows and columns pandas DataFrame Pandas find row where values for column is. News about the dynamic, interpreted, interactive, object-oriented, extensible programming language Python. row(key)`` and ``Frame. Re: VBA looping through cells in a column to check for a match It's not clear what you want to do if a match is found nor how you pass the ID number you want to search for into a variable in VBA. It's much better to extract the underlying NumPy arrays and work with those. You should use the dtypes method to get the datatype for each column. 2 Mutability and copying of data. The columns are made up of pandas Series objects. Pandas DataFrame - Iterate Rows - iterrows() To iterate through rows of a DataFrame, use DataFrame. To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows. where() to replace only the strings longer than 4 characters along with the. iterrows Iterate over the rows of a DataFrame as (index, Series) pairs. Pandas Cheat Sheet — Python for Data Science Pandas is arguably the most important Python package for data science. Adding a new column by passing as Series: one two three a 1. "iloc" in pandas is used to select rows and columns by number, in the order that they appear in the data frame. This is probably one of the most common uses for the for. The following are code examples for showing how to use pandas. (For spot 1, I want to plot points (12, 14), (26, 6) and 17, 19)) What is the best way to do this?. When you want to iterate over the rows of a DataFrame, you first have to transpose (T) the DataFrame. The first two are ways to apply column-wise functions on a dataframe column: use_column: use pandas column. Different ways 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. how to loop through each row of dataFrame in pyspark - Wikitechy mongodb (112) node. Iterating With Python Lambdas iterate over x, multiply the odd values by 5 and add them to the list y. Note that apply is just a little bit faster than a python for loop ! That's why it is most recommended using pandas builtin ufuncs for applying preprocessing tasks on columns (if a suitable ufunc is available for your task). apply(): Apply. iterrows(): iterate over DataFrame rows as (index, pd. We saw an example of this in the last blog post. 0 and 12) and invalid values (everything else, including nan values). Like what has been mentioned before, pandas object is most efficient when process the whole array at once. The types are being converted in your second method because that's how numpy arrays (which is what df. Python | Delete rows/columns from DataFrame using Pandas. You have reached the end of our Python dictionary tutorial! Complete your learning by taking DataCamp's the free Intro to Python for Data Science course to learn more about the Python basics that you need to know to do data science and the Intermediate Python for Data Science course to learn more about the control flow. So we added a column to the data frame called 'prediction' and by default set it all to 0. If the same variable value in one column repeats through several rows, it is more convenient just leave the later entries blank, rather than keep copying. To be able to support Pandas UDFs Spark will build up a batch of data and send it to python for processing, and then get a batch of data back as a result. JQuery has given a function each() from which we can iterate through any collection, but in this tutorial we will use this function to iterate through the gridview. If you want to do a row sum in pandas, given the dataframe df: df. Series`` corresponding to the row/column - ``Frame. Let's use this on the Planets data, for now dropping rows with missing values:. All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. Method #1 : In this method we will use re. I have done a short test to see which one of the three is the least time consuming. Download with Google Download with Facebook or download with email. sum() on 50 million rows, it takes around 65 milliseconds on my ~2015 macbook. Ultimately, I'd like a one-liner to plot all bar charts of all categorical variables. name: str or None, default "Pandas" The name of the returned namedtuples or None to return regular tuples. Pandas uses a separate mapping dictionary that maps the integer values to the raw ones. 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. Welcome - [Instructor] A Series is a one-dimensional array of indexed data. values #then you can. This example is a little more complicated so we'll need to think through a strategy for detecting these types of missing values. Series`` corresponding to the row/column - ``Frame. Optimum approach for iterating over a DataFrame examine different ways in which we can replace null values in a column. xls) Documents Using Python's xlrd; In this case, I've finally bookmarked it:). When your application calls engine. One strength of DataFrames from my perspective are the filtering capabilities. 1 day ago · I want to iterate through one of the columns within each of the worksheets and add the rows to a new list (or column). which I am not covering here. Suppose we have some JSON data: [code]json_data = { "name": { "first": ". I have searched extensively and tried to use arcpy. I am trying to run a Python script that loops through all fields in my input feature attribute table. NaN with the median of all other values in that data column. ['target_value'] # Iterate through columns, dispatching and transforming each feature. This is not guaranteed to work in all cases. You would be iterating through each row and then accessing the appropriate column as a key, in this way you can compare column to each other in each row. how to loop through each row of dataFrame in pyspark - Wikitechy mongodb (112) node. In this example we are going to loop through all the emails in predefined Outlook folder and collect some details of the emails which have a specific attachment. Update Pandas Dataframe with For Loop (self. values is) work. How to Read Excel files in Java using Apache POI Rajeev Singh • Java • Dec 23, 2017 • 6 mins read Excel files (spreadsheets) are widely used by people all over the world for various tasks related to organization, analysis, and storage of tabular data. Pandas DataFrame Exercises, Practice and Solution: Write a Pandas program to iterate over rows in a DataFrame. 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. To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows. Iterate over (column name, Series) pairs. 0 d NaN 4 NaN NaN. buffer_info ¶ Return a tuple (address, length) giving the current memory address and the length in elements of the buffer used to hold array’s contents.