Pandas groupby multiple columns


  • Pandas: How to group a dataframe by one or multiple columns?
  • Fun with Pandas Groupby, Aggregate, Multi-Index and Unstack
  • Python Pandas Groupby Tutorial
  • Use Pandas Groupby to Group and Summarise DataFrames
  • Python Pandas groupby multiple columns and append
  • Combining multiple columns in Pandas groupby with dictionary
  • Pandas: How to group a dataframe by one or multiple columns?

    Every time I do this I start from scratch and solved them in different ways. The high level problem is pretty simple and it goes something like this.

    You have a dataframe and want to groupby more than one variables, compute some summarized statistics using the remaining variables and use them to do some analysis. Typically plotting something really quick. You can easily imagine a number of variants of this problems.

    One of the pain points for me is lack of full understanding multi-indexing operations that Pandas enables. So far I have skipped dealing with multi-indexes and do not see myself confronting anytime soon It is useful for pivot like operation.

    Let us work through an example of this with gapminder dataset. We have two indices followed by three columns with average values, but with the original column names.

    Note that it gives three column names, not the first two index names. One nagging issue is that using mean function on grouped dataframe has the same column names.

    Although now we have mean values of the three columns. One can manually change the column names. Another option is to use Pandas agg function instead of mean.

    With agg function, we need to specify the variable we need to do summary operation. In this example, we have three variables and we want to compute mean. We can specify that as a dictionary to agg function. We again get a multi-indexed dataframe with continent and year as indices and three columns. And it looks like this. Pandas grouplby multiple variables: mean with agg Accessing Column Names and Index names from Multi-Index Dataframe Let us check the column names of the resulting dataframe.

    Now we get a MultiIndex names as a list of tuples. Each tuple gives us the original column name and the name of aggregation operation we did. In this example, we used mean. It can be other summary operations as well. With columns. Here we get the values of the first index. Here we combine them to create new column names using Pandas map function. And we get a simple dataframe with right column names. Typically, one might be interested in summary value of a single column, and making some visualization using the index variables.

    Let us take the approach that is similar to above example using agg function. Here we compute median life expectancy for each year and continent. We also create new appropriate column name as above. And we get our data in wide form. When you groupby multiple variables, by default the last level will be on the rows in the wide form. In this example below, we make a line plot again between year and median lifeExp for each continent.

    Multi-group line plot from wide data: Pandas plot Share this:.

    Fun with Pandas Groupby, Aggregate, Multi-Index and Unstack

    More specifically, we are going to learn how to group by one and multiple columns. Furthermore, we are going to learn how calculate some basics summary statistics e. In the image above we can see that we have, at least, three variables that we can group our data by. Of course, we could also group it by yrs. As previously mentioned we are going to use Pandas groupby to group a dataframe based on one, two, three, or more columns. Data can be loaded from other file formats as well e.

    There are many different methods that we can use on Pandas groupby objects and Pandas dataframe objects. In the following examples we are going to use some of these methods. In the next code example we are going to select the Assistant Professor group i. Thus, this is a way we can explore the dataset and see if there are any missing values in any column.

    We will return to this, later, when we are grouping by multiple columns. Now we are going to In some cases we may want to find out the number of unique values in each group. In this example we have a complete dataset and we can see that some have the same salary e. As we will see if we have missing values in the dataframe we would get a different result. This parameter, however, can only be used on Pandas series objects and not dataframe objects.

    In the following examples we are going to work with Pandas groupby to calculate the mean, median, and standard deviation by one group. We can calculate the mean and median salary, by groups, using the agg method. For instance, if we wanted to calculate the harmonic and geometric mean we can use SciPy: from scipy. To use Pandas groupby with multiple columns we add a list containing the column names. In this, and the next, Pandas groupby example we are going to use the apply method together with the lambda function.

    We are going to continue with calculating the percentage of men and women in each group i. We can, for instance, see that there are more male professors regardless of discipline. In this example, however, we are going to calculate the mean values per the three groups. We are not going into detail on how to use mean, median, and other methods to get summary statistics, however.

    That is, we are going to calculate mean, median, and standard deviation using the agg method. In this groupby example we are also adding the summary statistics i.

    Otherwise we will get a multi-level indexed result like the image below: If we use Pandas columns and the method ravel together with list comprehension we can add the suffixes to our column name and get another table. Note, in the example code below we only print the first 7 columns. In fact, with many columns it may be better to keep the result multi-level indexed. Additionally, as previous mentioned, we can also use custom functions, NumPy and SciPy methods when working with groupby agg.

    Just scroll back up and look at those examples, for grouping by one column, and apply them to the data grouped by multiple columns. More information of the different methods and objects used here can be found in the Pandas documentation. Conclusion: In this Pandas groupby tutorial we have learned how to use Pandas groupby to: group one or many columns count observations using the methods count and size calculate simple summary statistics using: groupby mean, median, std agg with our own function Calculate the percentage of observations in different groups The post Python Pandas Groupby Tutorial appeared first on Erik Marsja.

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    Python Pandas Groupby Tutorial

    Although now we have mean values of the three columns. One can manually change the column names. Another option is to use Pandas agg function instead of mean. With agg function, we need to specify the variable we need to do summary operation.

    Use Pandas Groupby to Group and Summarise DataFrames

    In this example, we have three variables and we want to compute mean. We can specify that as a dictionary to agg function. We again get a multi-indexed dataframe with continent and year as indices and three columns. And it looks like this. Pandas grouplby multiple variables: mean with agg Accessing Column Names and Index names from Multi-Index Dataframe Let us check the column names of the resulting dataframe. Now we get a MultiIndex names as a list of tuples.

    Each tuple gives us the original column name and the name of aggregation operation we did.

    Python Pandas groupby multiple columns and append

    In this example, we used mean. As a rule of thumb, if you calculate more than one column of results, your result will be a Dataframe. For a single column of results, the agg function, by default, will produce a Series. You can change this by selecting your operation column differently: produces Pandas Series data. The aggregation functionality provided by the agg function allows multiple statistics to be calculated per group in one calculation.

    Applying a single function to columns in groups Instructions for aggregation are provided in the form of a python dictionary or list. For example: Group the data frame by month and item and extract a number of stats from each group data. See below: Group the data frame by month and item and extract a number of stats from each group data. Remember that you can pass in custom and lambda functions to your list of aggregated calculations, and each will be passed the values from the column in your grouped data.

    Combining multiple columns in Pandas groupby with dictionary

    Python tuples are used to provide the column name on which to work on, along with the function to apply. We will return to this, later, when we are grouping by multiple columns. Now we are going to In some cases we may want to find out the number of unique values in each group. In this example we have a complete dataset and we can see that some have the same salary e. As we will see if we have missing values in the dataframe we would get a different result. This parameter, however, can only be used on Pandas series objects and not dataframe objects.

    In the following examples we are going to work with Pandas groupby to calculate the mean, median, and standard deviation by one group. We can calculate the mean and median salary, by groups, using the agg method. For instance, if we wanted to calculate the harmonic and geometric mean we can use SciPy: from scipy.

    To use Pandas groupby with multiple columns we add a list containing the column names. In this, and the next, Pandas groupby example we are going to use the apply method together with the lambda function. We are going to continue with calculating the percentage of men and women in each group i.


    thoughts on “Pandas groupby multiple columns

    • 26.08.2021 at 20:42
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      Quite right! It seems to me it is very excellent idea. Completely with you I will agree.

      Reply
    • 28.08.2021 at 01:55
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      In it something is. Clearly, thanks for the help in this question.

      Reply

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