The Example. The similarity between groupby, pivot_table, and crosstab. In SQL Server 2000 there was not a simple way to create cross-tab queries, but a new option first introduced in SQL Server 2005 has made this a bit easier. With aggregate functions, we can execute more complicated SQL queries. On the whole, the code for operations of pandas’ df is more concise than R’s df. Therefore, if you are just stepping into this field. Pandas - Groupby or Cut dataframe to bins? My df looks something like. An important thing to note about a pandas GroupBy object is that no splitting of the Dataframe has taken place at the point of creating the object. The numpy module is excellent for numerical computations, but to handle missing data or arrays with mixed types takes more work. Pandas groupby. View this notebook for live examples of techniques seen here. Stacked bar plot with two-level group by, normalized to 100%. This lesson of the Python Tutorial for Data Analysis covers grouping data with pandas. It's true that your Pandas code is unlikely to reach the calculation speeds of, say, fully optimized raw C code. How to Rename Columns in Pandas? One can change the column names of a pandas dataframe in at least two ways. The data is categorical, like this: var1 var2 0 1 1 0 0 2 0 1 0 2 He. Pandas writes Excel files using the Xlwt module for xls files and the Openpyxl or XlsxWriter modules for xlsx files. The training will include the following; Installing Jupyter. Some users might be surprised to find that agroupby. The groupby syntax is also more descriptive, the count aggregation function appended to the groupby call clearly states the operation being performed. Reading sniffed SSL/TLS traffic from curl with Wireshark less than 1 minute read If you want to debug/inspect/analyze SSL/TLS traffic made by curl, you can easily do so by setting the environment variable SSLKEYLOGFILE to a file path of y. This way, I really wanted a place to gather my tricks that I really don't want to forget. Introduction. Calculating Totals in Access Queries. groupby() function is used to split the data into groups based on. Grouped aggregate Pandas UDFs are used with groupBy(). What's more, with Pandas and Python, your original data frame remains in memory. The following are code examples for showing how to use pandas. Python Pandas Tutorial. This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results. In this post you will discover some quick and dirty. 6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. It has not actually computed anything yet except for some intermediate data about the group key df['key1']. Tag: pandas Pandas Data Structures Pandas Filter Filtering rows of a DataFrame is an almost mandatory task for Data Analysis with Python. Slicing R R is easy to access data. In this article we discuss how to get a list of column and row names of a DataFrame object in python pandas. groupby() method that works in the same way as the SQL group by. You may want to pass sort=False for potential speedup:. Series represents a column within the group or window. The dimensions of the crosstab refer to the number of rows and columns in the table. This is accomplished in Pandas using the "groupby()" and "agg()" functions of Panda's DataFrame objects. crosstab交叉表. In this case the person name is the level 0 of the index and the activity is on level 1. And with the power of data frames and packages that operate on them like reshape, my data manipulation and aggregation has moved more and more into the R world as well. Tengo el siguiente dataframe: df = pd. The Example. Pandas is a powerful Python package that can be used to perform statistical analysis. In this page, we are going to discuss the usage of GROUP BY and ORDER BY along with the SQL COUNT() function. First of all, create a DataFrame object of students records i. 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. Here are just a few of the things that pandas does well: Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects Automatic and explicit data alignment: objects can be explicitly aligned to a set of. In order to perform slicing on data, you need a data frame. APPLIES TO: SQL Server Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse A SELECT statement clause that divides the query result into groups of rows, usually for the purpose of performing one or more aggregations on each group. Grouped aggregate Pandas UDFs are used with groupBy(). Moving averages, ranks, etc. xlsx files with a single call to pd. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. joining two crosstab queries If this is your first visit, be sure to check out the FAQ by clicking the link above. Future versions of pandas_datareader will end support for Python 2. Most data operations are done on groups defined by variables. I couldn't be more pleased with the success of this implementation between Swanson Health Products and GroupBy. groupByKey() operates on Pair RDDs and is used to group all the values related to a given key. ungroup() removes grouping. This article explains a series of tips for crosstab queries. A Sample DataFrame. The data actually need not be labeled at all to be placed into a pandas data structure; The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. Pandas was built to ease data analysis and manipulation. 50+) and self-reported general health status (HLTHSTAT, collapsed from HAB1 as excellent/very good, good, or fair/poor). The following are code examples for showing how to use pandas. It contains high-level data structures and manipulation tools designed to make data analysis fast and easy. SUM is used with a GROUP BY clause. key will become Column Name and list in the value field will be the column data i. Calculating Totals in Access Queries. An overview of Pandas, a Python library, which is old but gold and a must-know if you're attempting to do any work with data in the Python world, and a glance of Seaborn, a Python library for making statistical visualizations. python - How can I create a Pivot Table that show sum() of group values, using my Pandas Data Frame? python - MultiIndex Group By in Pandas Data Frame; python - How to apply a concat function to a group by data frame using pandas? python - How to subset a data frame using Pandas based on a group criteria?. crosstab (index = df_tips [ 'day' ], columns = df_tips [ 'sex' ]). You can vote up the examples you like or vote down the ones you don't like. Personal website for Shane Lynn PhD, machine learning researcher and entrepreneur. The GroupBy object simply has all of the information it needs about the nature of the grouping. - [Instructor] Groupby is one…of the most important functionalities available in Pandas. Pandas/Python has an even more powerful function, aggregate (or simply agg). Pandas is a powerful data analysis Python library that is built on top of numpy which is yet another library that let’s you create 2d and even 3d arrays of data in Python. cut categorical variable Tag: python , pandas I have a data frame that is an output from groupby using a categorical variable created by pd. …Now, many people when they first learn…how to use the Groupby function,…don't know what to do with the. At a certain point, you realize that you’d like to convert that pandas DataFrame into a list. groupby(), using lambda functions and pivot tables, and sorting and sampling data. iloc[, ], which is sure to be a source of confusion for R users. 'groupby' multiple columns and 'sum' multiple columns with different types #13821 pmckelvy1 opened this issue Jul 27, 2016 · 7 comments · Fixed by #18953 Comments. Parallelize Pandas map() or apply() Pandas is a very useful data analysis library for Python. groupby(by) Tabular Data and pandas: Return a GroupBy object that contains a DataFrame grouped by the values in the specified columns by: GroupBy. They are − Splitting the Object. Data in pandas is stored in dataframes, its analog of spreadsheets. You can assign either a single hierarchy or any number of categories to each column and row role. Often, you’ll want to organize a pandas DataFrame into subgroups for further analysis. Requires basic macro, coding, and interoperability skills. It ran smoothly and so then I tried running it with the huge file of data. Combining the results. Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby) - Duration: 1:00:27. In our case, these are pandas, which provides data-structures, the tools to handle them and I/O utilities to read and write from and to different datasources, and matplotlib, which we will use to create the charts. You may have to register before you can post: click the register link above to proceed. While doing that, we look at analogies between Pandas and SQL, a standard in relational databases. Can be any function valid in a groupby context fill_value Replace missing values in result table margins Add row/column subtotals and grand total, False by default Cross-Tabulations: Crosstab A cross-tabulation (or crosstab for short) is a special case of a pivot table that computes group frequencies. The iloc indexer syntax is data. We start by importing pandas, numpy and creating a dataframe:. Python Pandas - Comparison with SQL - Since many potential Pandas users have some familiarity with SQL, this page is meant to provide some examples of how various SQL operations can be performed usi. 利用python的pandas库进行数据分组分析十分便捷，其中应用最多的方法包括：groupby、pivot_table及crosstab，以下分别进行介绍。. value_counts() which does not work because value_counts operates on the groupby series and not a dataframe. This is a post about R and pandas and about what I've learned about each. Returns a nice data table as a Pandas DataFrame that includes the variable name, total number of non-missing observations, standard deviation, standard error, and the 95% confidence interval. The dimensions of the crosstab refer to the number of rows and columns in the table. A crosstab query is a matrix, where the column headings come from the values in a field. Pandas offers several options for grouping and summarizing data but this variety of options can be a blessing and a curse. …It splits a DataFrame into groups…based on some criteria,…it applies a function to each group independently…and it combines the results into a DataFrame. loc[('G', 'P'), :] However, it is possible that for such indexing operations, the multi-index needs to be sorted (otherwise it can give and exception). groupby(['label']). Pandas分组统计函数：groupby、pivot_table及crosstab 02-28 阅读数 5万+ 利用python的pandas库进行数据分组分析十分便捷，其中应用最多的方法包括：groupby、pivot_table及crosstab，以下分别进行介绍。. If you’re a using the Python stack for machine learning, a library that you can use to better understand your data is Pandas. Update: Pandas version 0. Access Group By Clause. We took a look at how to create cross-tab queries in SQL Server 2000 in this previous tip and in this tip we will look at the SQL Server PIVOT. It looks like I have to group by and then count values, so I tried that with df. ) The table dimensions are reported as as RxC, where R is the number of categories for the row variable, and C is the number of categories for the column variable. groupby('release_year') This creates a groupby object: # Check type of GroupBy object type(df_by_year) pandas. crosstab()関数を使うとクロス集計分析ができる。 カテゴリデータ（カテゴリカルデータ、質的データ）のカテゴリごとのサンプル数（出現回数・頻度）の算出などが可能。 pandas. What is Crosstab? Definition. This process involves three steps. In the SAS-Callable SUDAAN code below (Exhibit 1), the following statements are used:. Jaspersoft Studio can handle simple tables or crosstabs. Any groupby operation involves one of the following operations on the original object. Finally, a solution using an integers table is:. Selecting pandas data using "iloc" The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position. At a certain point, you realize that you’d like to convert that pandas DataFrame into a list. This page is based on a Jupyter/IPython Notebook: download the original. In this article we discuss how to get a list of column and row names of a DataFrame object in python pandas. These Pandas DataFrames may live on disk for larger-than-memory computing on a single machine, or on many different machines in a cluster. The diagnosis of PANDAS is a clinical diagnosis, which means that there are no lab tests that can diagnose PANDAS. Thus, in the first example we are going to group the data by sex and get the mean age, piq, and viq. Method Chaining. Source code for pandas. The idea is that this object has all of the information needed to then apply some operation to each of the groups. Pandas crosstab margins double counting if values specifies a different field than rows/cols #4003. groupby A label or list of labels may be passed to group by the columns in self. In crosstab query, we need to assign at least one row heading, one column heading and a value. This is the split in split-apply-combine: # Group by year df_by_year = df. groupby() method that works in the same way as the SQL group by. Pandas provides a similar function called (appropriately enough) pivot_table. An important thing to note about a pandas GroupBy object is that no splitting of the Dataframe has taken place at the point of creating the object. value_counts() which does not work because value_counts operates on the groupby series and not a dataframe. And: While GroupBy can index elements by keys, a Dictionary can do this and has the performance advantages provided by hashing. However, apply is more native to different libraries and therefore, quite different between libraries. Pandas groupby. DataFrameNaFunctions Methods for handling missing data (null values). 'groupby' multiple columns and 'sum' multiple columns with different types #13821 pmckelvy1 opened this issue Jul 27, 2016 · 7 comments · Fixed by #18953 Comments. At its core, it is. In many situations, we split the data into sets and we apply some functionality on each subset. Due to their similar appearance, crosstabs and pivot tables are often referred to as the same thing. Notice that a tuple is interpreted as a (single) key. We are very excited about the future and where GroupBy can take us. Generating Frequency Tables. Few tools hold a candle to pandas when it comes to Split-Apply-Combine operations. This is the split in split-apply-combine: # Group by year df_by_year = df. Let's have a look at a single grouping with the adult dataset. Select the n most frequent items from a pandas groupby dataframe. size vs series. Python for Data AnalysisAndrew HenshawGeorgia Tech Research Institute 2. The following are code examples for showing how to use pandas. mean(), count() pd. Just like @janscas I'm using categoricals for memory savings as advised by the docs, but I periodically try to groupby a categorical column and blow up my memory because pandas wants to generate a result filled with tons of NaNs. The data is categorical, like this: var1 var2 0 1 1 0 0 2 0 1 0 2 He. Tabular Data and pandas. Mon May 2, 2005 by Jeff Smith in t-sql, crosstabs-pivoting-data, code-library-sql. A crosstab query calculates a sum, average, or other aggregate function, and then groups the results by two sets of values— one set on the side of the datasheet and the other set across the top. The similarity between groupby, pivot_table, and crosstab. What is a CrossTab Query? A cross tab query is a transformation of rows of data to columns. Let's check out how our data is distributed. Pandas crosstab margins double counting if values specifies a different field than rows/cols #4003. Parallelize Pandas map() or apply() Pandas is a very useful data analysis library for Python. It has not actually computed anything yet except for some intermediate data about the group key df['key1']. It can be very useful for handling large amounts of data. It contains high-level data structures and manipulation tools designed to make data analysis fast and easy. Source code for pandas. It looks like I have to group by and then count values, so I tried that with df. DataFrameGroupBy Step 2. However, I was dissatisfied with the limited expressiveness (see the end of the article), so I decided to invest some serious time in the groupby functionality …. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed. Pandas - Groupby or Cut dataframe to bins? Close. To operate on more granular aggregate data, we can use the following two clauses: GROUP BY takes a list of columns and groups the table like the pd. DataFrame, pandas. Calculating Totals in Access Queries. (The "total" row/column are not included. SELECT title, count(1) FROM lens GROUP BY title ORDER BY 2 DESC LIMIT 25; Alternatively, pandas has a nifty value_counts method - yes, this is simpler - the goal above was to show a basic groupby example. In the crosstab query, which is a special type of Totals query, the Total row that appears in the query design grid will always be active. Check memory usage. Tabular Data and pandas: Apply a function to each group in a GroupBy object GroupBy; e. It uses a process of creating contingency tables from the multivariate frequency distribution of variables, presented in a matrix. No aggregation will take place until we explicitly call an aggregation function on the GroupBy object. Every frame has the module query() as one of its objects members. groupby(['id', 'group']). Various Pandas functionalities make data preprocessing extremely simple. cut categorical variable Tag: python , pandas I have a data frame that is an output from groupby using a categorical variable created by pd. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed. The measured value is the median execution time of pandas relative to the median execution time of data. GROUP BY returns one records for each group. Enter Pandas, which is a great library for data analysis. Pandas: Stack/Unstack, Pivot_table & CrossTab. Crosstab or Cross Tabulation is used to aggregate and jointly display the distribution of two or more variables by tabulating their results one against the other in 2-dimensional grids. In this case the person name is the level 0 of the index and the activity is on level 1. We show that some rather simple analytics allow us to attain a reasonable score in an interesting Kaggle competition. Often, you’ll want to organize a pandas DataFrame into subgroups for further analysis. First off, before going any further make sure you have read the hall of fame SQLTeam article by Rob Volk on generating crosstab results using a flexible, dynamic stored procedure that has been viewed over 100,000 times!. These examples make use of the odo library. A Sample DataFrame. See the Package overview for more detail about what's in the library. pandas groupby enables transformations, aggregations, and easy. crosstab 을 사용할 수 있습니다 : Pandas에서 groupby를 사용하여 한 열의 항목을 다른 열과 비교하여 계산하십시오. Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby) - Duration: 1:00:27. 5 to 2 times longer than the equivalent data. pandas Multi-index and groupbys (article) - DataCamp. pandas Multi-index and groupbys (article) - DataCamp. The SQL GROUP BY Statement. cut categorical variable Tag: python , pandas I have a data frame that is an output from groupby using a categorical variable created by pd. We are very excited about the future and where GroupBy can take us. But what I want eventually is another DataFrame object that contains all the rows in the GroupBy object. pandas - Python Data Analysis 1. count and printing yields a GroupBy object: City Name Name City Alice Seattle 1 1 Bob Seattle 2 2 Mallory Portland 2 2 Seattle 1 1. Tengo el siguiente dataframe: df = pd. frame(a=rnorm(5), b=rnorm(5), c=rnorm(5), d=rnorm(5), e=rnorm(5)) df[, c("a", "c","e")] or. ©2019 UCBerkeley RISELab. , a DataFrame column name. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. One way to rename columns in Pandas is to use df. Telerik Reporting provides three items that can be used as templates and you can add them directly from the Toolbox: table, crosstab, and list. The data actually need not be labeled at all to be placed into a pandas data structure; The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. I'm trying to groupby ID first, and count the number of unique values of outcome within that ID. Few tools hold a candle to pandas when it comes to Split-Apply-Combine operations. GROUP BY returns one records for each group. crosstab — pandas 0. groupby(), using lambda functions and pivot tables, and sorting and sampling data. Some of the common operations for data manipulation are listed below: Now, let us understand all these operations one by one. Use the table to display fields from a dataset either as detail data or as grouped data in a grid or free-form layout. ‘’ 没有指定values，默认为count数量， 列 行. 利用python的pandas库进行数据分组分析十分便捷，其中应用最多的方法包括：groupby、pivot_table及crosstab，以下分别进行介绍。. Pandas is a powerful data analysis Python library that is built on top of numpy which is yet another library that let’s you create 2d and even 3d arrays of data in Python. In this tutorial, I'll show you a full example of a Confusion Matrix in Python. As usual let's start by creating a dataframe. Pandas is a foundational library for analytics, data processing, and data science. pandasql supports aggregation. It's useful when building machine learning models which may require a lot memory in training. Source code for pandas. One of the major benefits of using Python and pandas over Excel is that it helps you automate Excel file processing by writing scripts and integrating with your automated data workflow. We start with groupby aggregations. …Groupby does three things. Examples on how to plot data directly from a Pandas dataframe, using matplotlib and pyplot. Combining the results. Dropping rows and columns in Pandas. [code ]pivot()[/code] is used for pivoting without aggregation. Combining the results. • PowerPoint• IPython (ipython –pylab=inline)• Custom bridge (ipython2powerpoint) 3. First of all, create a DataFrame object of students records i. 利用python的pandas库进行数据分组分析十分便捷，其中应用最多的方法包括：groupby、pivot_table及crosstab，以下分别进行介绍。. It can be very useful for handling large amounts of data. Data in pandas is stored in dataframes, its analog of spreadsheets. How pandas uses matplotlib plus figures axes and subplots. Counting the number of observations by regiment and category. groupby() method. At the present time, the clinical features of the illness are the only means of determining whether a child might have PANDAS. When used with unpaired data, the key for groupBy() is decided by the function literal passed to the method Example. This is the split in split-apply-combine: # Group by year df_by_year = df. The iloc indexer syntax is data. (The "total" row/column are not included. DataFrame, pandas. Pandas is the most widely used tool for data munging. Seriesのgroupby()メソッドでデータをグルーピング（グループ分け）できる。グループごとにデータを集約して、それぞれの平均、最小値、最大値、合計などの統計量を算出したり、任意の関数で処理したりすることが可能。. Finally, a solution using an integers table is:. The Pandas module is a high performance, highly efficient, and high level data analysis library. groupby('release_year') This creates a groupby object: # Check type of GroupBy object type(df_by_year) pandas. It is quite high level, so you don't have to muck about with low level details, unless you really want to. A crosstab query calculates a sum, average, or other aggregate function, and then groups the results by two sets of values— one set on the side of the datasheet and the other set across the top. groupByKey() operates on Pair RDDs and is used to group all the values related to a given key. Tag: pandas Pandas Data Structures Pandas Filter Filtering rows of a DataFrame is an almost mandatory task for Data Analysis with Python. Let’s get started. In crosstab query, we need to assign at least one row heading, one column heading and a value. Think of SQL's GROUP BY. Pandas: Stack/Unstack, Pivot_table & CrossTab. In our case, these are pandas, which provides data-structures, the tools to handle them and I/O utilities to read and write from and to different datasources, and matplotlib, which we will use to create the charts. The crosstab performance issue of using DMR model for sparse data has been confirmed by IBM Cognos. Method Chaining. groupby A label or list of labels may be passed to group by the columns in self. Often, you’ll want to organize a pandas DataFrame into subgroups for further analysis. By default, cells in a group row or column that contain simple expressions that do not include an aggregate function, evaluate to the first value in the group. Now if we want to show the marks obtained in different courses by each student, we need to change the names of the students in to column or field heading, and course name in to row heading to get a brief but complete view of the records. R provides many methods for creating frequency and contingency tables. An important thing to note about a pandas GroupBy object is that no splitting of the Dataframe has taken place at the point of creating the object. There is a similar command, pivot, which we will use in the next section which is for reshaping data. csv or excel. iloc[, ], which is sure to be a source of confusion for R users. Examples on how to plot data directly from a Pandas dataframe, using matplotlib and pyplot. Pandas offers two methods of summarising data - groupby and pivot_table*. Pandas groupby. In particular, it provides elegant, functional, chainable syntax in cases where pandas would require mutation, saved intermediate values, or other awkward constructions. The only hitch with the two above approaches is that some databases may not let you use the column alias "quarter" in the GROUP BY clause, so you would have to repeat the CASE construct or the CAST function in the GROUP BY clause. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. Runtime comparison of pandas crosstab, groupby and pivot_table. 0 by libraries cluster pypi time series json method csv files html nlp bokeh aws s3 groupby excel widget. Filter using query. The dimensions of the crosstab refer to the number of rows and columns in the table. One is neurologist Roger Kurlan, M. Data in pandas is stored in dataframes, its analog of spreadsheets. Pandas is gives to the names of the rows and columns of the matrix like data. "iloc" in pandas is used to select rows and columns by number, in the order that they appear in the data frame. Rank the dataframe in python pandas - (min, max, dense & rank by group) In this tutorial we will learn how to rank the dataframe in python pandas by ascending and descending order with maximum rank value, minimum rank value , average rank value and dense rank. (The "total" row/column are not included. Grouped aggregate pandas UDFs are similar to Spark aggregate functions. Python Pandas - Comparison with SQL - Since many potential Pandas users have some familiarity with SQL, this page is meant to provide some examples of how various SQL operations can be performed usi. Suppose you have a dataset containing credit card transactions, including: the date of the transaction; the credit card number; the type of the expense. Pandas的groupby()功能很强大，用好了可以方便的解决很多问题，在数据处理以及日常工作中经常能施展拳脚。 今天，我们一起来领略下groupby()的魅力吧。 首先，引入相关package： import pandas as pd import numpy as np groupby的基础操作. Pandas objects can be split on any of their axes. Slicing R R is easy to access data. Moving averages, ranks, etc. …Now, many people when they first learn…how to use the Groupby function,…don't know what to do with the. This way, I really wanted a place to gather my tricks that I really don't want to forget. frame(a=rnorm(5), b=rnorm(5), c=rnorm(5), d=rnorm(5), e=rnorm(5)) df[, c("a", "c","e")] or. Like many, I often divide my computational work between Python and R. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. It takes a number of arguments. totals broken down by months, products etc. This is multi index, a valuable trick in pandas dataframe which allows us to have a few levels of index hierarchy in our dataframe. Pandas is a powerful Python package that can be used to perform statistical analysis. Often, you’ll want to organize a pandas DataFrame into subgroups for further analysis. For instance, Spark DataFrame has groupBy, Pandas DataFrame has groupby. apply() acceptant la même lambda retournerait les mêmes résultats. Few tools hold a candle to pandas when it comes to Split-Apply-Combine operations. GroupBy Conference. Construct a Cross-Tabulation summary of CustomerID vs ProductNo and the total number of products the company ordered for that product in the month of January. Introducing Pandas DataFrame for Python data analysis The open source library gives Python the ability to work with spreadsheet-like data for fast data loading, manipulating, aligning, and merging. '' 没有指定values，默认为count数量， 列 行.