python excel 文件合并

Combining Data From Multiple Excel Files

Introduction

A common task for python and pandas is to automate the process of aggregating data from multiple files and spreadsheets.

This article will walk through the basic flow required to parse multiple Excel files, combine the data, clean it up and analyze it. The combination of python + pandas can be extremely powerful for these activities and can be a very useful alternative to the manual processes or painful VBA scripts frequently used in business settings today.

The Problem

Before, I get into the examples, here is a simple diagram showing the challenges with the common process used in businesses all over the world to consolidate data from multiple Excel files, clean it up and perform some analysis.

If you’re reading this article, I suspect you have experienced some of the problems shown above. Cutting and pasting data or writing painful VBA code will quickly get old. There has to be a better way!

Python + pandas can be a great alternative that is much more scaleable and powerful.

By using a python script, you can develop a more streamlined and repeatable solution to your data processing needs. The rest of this article will show a simple example of how this process works. I hope it will give you ideas of how to apply these tools to your unique situation.

Collecting the Data

If you are interested in following along, here are the excel files and a link to the notebook:

• Jan 2014
• Feb 2014
• Mar 2014
• status
• notebook

The first step in the process is collecting all the data into one place.

First, import pandas and numpy

import pandas as pd
import numpy as np

　　

Let’s take a look at the files in our input directory, using the convenient shell commands in ipython.

!ls ../in
address-state-example.xlsx  report.xlsx                sample-address-new.xlsx
customer-status.xlsx            sales-feb-2014.xlsx    sample-address-old.xlsx
excel-comp-data.xlsx            sales-jan-2014.xlsx    sample-diff-1.xlsx
my-diff-1.xlsx                  sales-mar-2014.xlsx    sample-diff-2.xlsx
my-diff-2.xlsx                  sample-address-1.xlsx  sample-salesv3.xlsx
my-diff.xlsx                    sample-address-2.xlsx
pricing.xlsx                    sample-address-3.xlsx

　　

There are a lot of files, but we only want to look at the sales .xlsx files.

!ls ../in/sales*.xlsx
../in/sales-feb-2014.xlsx  ../in/sales-jan-2014.xlsx  ../in/sales-mar-2014.xlsx

　　

Use the python glob module to easily list out the files we need.

import glob
glob.glob("../in/sales*.xlsx")
['../in/sales-jan-2014.xlsx',
 '../in/sales-mar-2014.xlsx',
 '../in/sales-feb-2014.xlsx']

　　

This gives us what we need. Let’s import each of our files and combine them into one file. Panda’s concat and append can do this for us. I’m going to use append in this example.

The code snippet below will initialize a blank DataFrame then append all of the individual files into the all_data DataFrame.

all_data = pd.DataFrame()
for f in glob.glob("../in/sales*.xlsx"):
    df = pd.read_excel(f)
    all_data = all_data.append(df,ignore_index=True)

　　

Now we have all the data in our all_data DataFrame. You can use describe to look at it and make sure you data looks good.

all_data.describe()

　　

	account number	quantity	unit price	ext price
count	1742.000000	1742.000000	1742.000000	1742.000000
mean	485766.487945	24.319173	54.985454	1349.229392
std	223750.660792	14.502759	26.108490	1094.639319
min	141962.000000	-1.000000	10.030000	-97.160000
25%	257198.000000	12.000000	32.132500	468.592500
50%	527099.000000	25.000000	55.465000	1049.700000
75%	714466.000000	37.000000	77.607500	2074.972500
max	786968.000000	49.000000	99.850000	4824.540000

A lot of this data may not make much sense for this data set but I’m most interested in the count row to make sure the number of data elements makes sense. In this case, I see all the data rows I expect.

all_data.head()

　　

	account number	name	sku	quantity	unit price	ext price	date
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2014-01-01 07:21:51
1	714466	Trantow-Barrows	S2-77896	-1	63.16	-63.16	2014-01-01 10:00:47
2	218895	Kulas Inc	B1-69924	23	90.70	2086.10	2014-01-01 13:24:58
3	307599	Kassulke, Ondricka and Metz	S1-65481	41	21.05	863.05	2014-01-01 15:05:22
4	412290	Jerde-Hilpert	S2-34077	6	83.21	499.26	2014-01-01 23:26:55

It is not critical in this example but the best practice is to convert the date column to a date time object.

all_data['date'] = pd.to_datetime(all_data['date'])

　　

Combining Data

Now that we have all of the data into one DataFrame, we can do any manipulations the DataFrame supports. In this case, the next thing we want to do is read in another file that contains the customer status by account. You can think of this as a company’s customer segmentation strategy or some other mechanism for identifying their customers.

First, we read in the data.

status = pd.read_excel("../in/customer-status.xlsx")
status

　　

	account number	name	status
0	740150	Barton LLC	gold
1	714466	Trantow-Barrows	silver
2	218895	Kulas Inc	bronze
3	307599	Kassulke, Ondricka and Metz	bronze
4	412290	Jerde-Hilpert	bronze
5	729833	Koepp Ltd	silver
6	146832	Kiehn-Spinka	silver
7	688981	Keeling LLC	silver
8	786968	Frami, Hills and Schmidt	silver
9	239344	Stokes LLC	gold
10	672390	Kuhn-Gusikowski	silver
11	141962	Herman LLC	gold
12	424914	White-Trantow	silver
13	527099	Sanford and Sons	bronze
14	642753	Pollich LLC	bronze
15	257198	Cronin, Oberbrunner and Spencer	gold

We want to merge this data with our concatenated data set of sales. Use panda’s merge function and tell it to do a left join which is similar to Excel’s vlookup function.

all_data_st = pd.merge(all_data, status, how='left')
all_data_st.head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2014-01-01 07:21:51	gold
1	714466	Trantow-Barrows	S2-77896	-1	63.16	-63.16	2014-01-01 10:00:47	silver
2	218895	Kulas Inc	B1-69924	23	90.70	2086.10	2014-01-01 13:24:58	bronze
3	307599	Kassulke, Ondricka and Metz	S1-65481	41	21.05	863.05	2014-01-01 15:05:22	bronze
4	412290	Jerde-Hilpert	S2-34077	6	83.21	499.26	2014-01-01 23:26:55	bronze

This looks pretty good but let’s look at a specific account.

all_data_st[all_data_st["account number"]==737550].head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
9	737550	Fritsch, Russel and Anderson	S2-82423	14	81.92	1146.88	2014-01-03 19:07:37	NaN
14	737550	Fritsch, Russel and Anderson	B1-53102	23	71.56	1645.88	2014-01-04 08:57:48	NaN
26	737550	Fritsch, Russel and Anderson	B1-53636	42	42.06	1766.52	2014-01-08 00:02:11	NaN
32	737550	Fritsch, Russel and Anderson	S1-27722	20	29.54	590.80	2014-01-09 13:20:40	NaN
42	737550	Fritsch, Russel and Anderson	S1-93683	22	71.68	1576.96	2014-01-11 23:47:36	NaN

This account number was not in our status file, so we have a bunch of NaN’s. We can decide how we want to handle this situation. For this specific case, let’s label all missing accounts as bronze. Use the fillna function to easily accomplish this on the status column.

all_data_st['status'].fillna('bronze',inplace=True)
all_data_st.head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2014-01-01 07:21:51	gold
1	714466	Trantow-Barrows	S2-77896	-1	63.16	-63.16	2014-01-01 10:00:47	silver
2	218895	Kulas Inc	B1-69924	23	90.70	2086.10	2014-01-01 13:24:58	bronze
3	307599	Kassulke, Ondricka and Metz	S1-65481	41	21.05	863.05	2014-01-01 15:05:22	bronze
4	412290	Jerde-Hilpert	S2-34077	6	83.21	499.26	2014-01-01 23:26:55	bronze

Check the data just to make sure we’re all good.

all_data_st[all_data_st["account number"]==737550].head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
9	737550	Fritsch, Russel and Anderson	S2-82423	14	81.92	1146.88	2014-01-03 19:07:37	bronze
14	737550	Fritsch, Russel and Anderson	B1-53102	23	71.56	1645.88	2014-01-04 08:57:48	bronze
26	737550	Fritsch, Russel and Anderson	B1-53636	42	42.06	1766.52	2014-01-08 00:02:11	bronze
32	737550	Fritsch, Russel and Anderson	S1-27722	20	29.54	590.80	2014-01-09 13:20:40	bronze
42	737550	Fritsch, Russel and Anderson	S1-93683	22	71.68	1576.96	2014-01-11 23:47:36	bronze

Now we have all of the data along with the status column filled in. We can do our normal data manipulations using the full suite of pandas capability.

Using Categories

One of the relatively new functions in pandas is support for categorical data. From the pandas, documentation:

Categoricals are a pandas data type, which correspond to categorical variables in statistics: a variable, which can take on only a limited, and usually fixed, number of possible values (categories; levels in R). Examples are gender, social class, blood types, country affiliations, observation time or ratings via Likert scales.

For our purposes, the status field is a good candidate for a category type.

Version Warning

You must make sure you have a recent version of pandas ( > 0.15) installed for this example to work.

pd.__version__
'0.15.2'

　　

First, we typecast it the column to a category using astype .

all_data_st["status"] = all_data_st["status"].astype("category")

　　

This doesn’t immediately appear to change anything yet.

all_data_st.head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2014-01-01 07:21:51	gold
1	714466	Trantow-Barrows	S2-77896	-1	63.16	-63.16	2014-01-01 10:00:47	silver
2	218895	Kulas Inc	B1-69924	23	90.70	2086.10	2014-01-01 13:24:58	bronze
3	307599	Kassulke, Ondricka and Metz	S1-65481	41	21.05	863.05	2014-01-01 15:05:22	bronze
4	412290	Jerde-Hilpert	S2-34077	6	83.21	499.26	2014-01-01 23:26:55	bronze

Buy you can see that it is a new data type.

all_data_st.dtypes
account number             int64
name                      object
sku                       object
quantity                   int64
unit price               float64
ext price                float64
date              datetime64[ns]
status                  category
dtype: object

　　

Categories get more interesting when you assign order to the categories. Right now, if we call sort on the column, it will sort alphabetically.

all_data_st.sort(columns=["status"]).head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
1741	642753	Pollich LLC	B1-04202	8	95.86	766.88	2014-02-28 23:47:32	bronze
1232	218895	Kulas Inc	S1-06532	29	42.75	1239.75	2014-09-21 11:27:55	bronze
579	527099	Sanford and Sons	S1-27722	41	87.86	3602.26	2014-04-14 18:36:11	bronze
580	383080	Will LLC	B1-20000	40	51.73	2069.20	2014-04-14 22:44:58	bronze
581	383080	Will LLC	S2-10342	15	76.75	1151.25	2014-04-15 02:57:43	bronze

We use set_categories to tell it the order we want to use for this category object. In this case, we use the Olympic medal ordering.

all_data_st["status"].cat.set_categories([ "gold","silver","bronze"],inplace=True)

　　

Now, we can sort it so that gold shows on top.

all_data_st.sort(columns=["status"]).head()

　　

	account number	name	sku	quantity	unit price	ext price	date	status
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2014-01-01 07:21:51	gold
1193	257198	Cronin, Oberbrunner and Spencer	S2-82423	23	52.90	1216.70	2014-09-09 03:06:30	gold
1194	141962	Herman LLC	B1-86481	45	52.78	2375.10	2014-09-09 11:49:45	gold
1195	257198	Cronin, Oberbrunner and Spencer	B1-50809	30	51.96	1558.80	2014-09-09 21:14:31	gold
1197	239344	Stokes LLC	B1-65551	43	15.24	655.32	2014-09-10 11:10:02	gold

Analyze Data

The final step in the process is to analyze the data. Now that it is consolidated and cleaned, we can see if there are any insights to be learned.

all_data_st["status"].describe()
count       1742
unique         3
top       bronze
freq         764
Name: status, dtype: object

　　

For instance, if you want to take a quick look at how your top tier customers are performaing compared to the bottom. Use groupbyto get the average of the values.

all_data_st.groupby(["status"])["quantity","unit price","ext price"].mean()

　　

	quantity	unit price	ext price
status
gold	24.680723	52.431205	1325.566867
silver	23.814241	55.724241	1339.477539
bronze	24.589005	55.470733	1367.757736

Of course, you can run multiple aggregation functions on the data to get really useful information

all_data_st.groupby(["status"])["quantity","unit price","ext price"].agg([np.sum,np.mean, np.std])

　　

	quantity			unit price			ext price
	sum	mean	std	sum	mean	std	sum	mean	std
status
gold	8194	24.680723	14.478670	17407.16	52.431205	26.244516	440088.20	1325.566867	1074.564373
silver	15384	23.814241	14.519044	35997.86	55.724241	26.053569	865302.49	1339.477539	1094.908529
bronze	18786	24.589005	14.506515	42379.64	55.470733	26.062149	1044966.91	1367.757736	1104.129089

So, what does this tell you? Well, the data is completely random but my first observation is that we sell more units to our bronze customers than gold. Even when you look at the total dollar value associated with bronze vs. gold, it looks odd that we sell more to bronze customers than gold.

Maybe we should look at how many bronze customers we have and see what is going on?

What I plan to do is filter out the unique accounts and see how many gold, silver and bronze customers there are.

I’m purposely stringing a lot of commands together which is not necessarily best practice but does show how powerful pandas can be. Feel free to review my previous article here and here to understand it better. Play with this command yourself to understand how the commands interact.

all_data_st.drop_duplicates(subset=["account number","name"]).ix[:,[0,1,7]].groupby(["status"])["name"].count()
status
gold      4
silver    7
bronze    9
Name: name, dtype: int64

　　

Ok. This makes a little more sense. We see that we have 9 bronze customers and only 4 customers. That is probably why the volumes are so skewed towards our bronze customers. This result makes sense given the fact that we defaulted to bronze for many of our customers. Maybe we should reclassify some of them? Obviously this data is fake but hopefully this shows how you can use these tools to quickly analyze your own data.

Conclusion

This example only covered the aggregation of 4 simple Excel files containing random data. However the principles can be applied to much larger data sets yet you can keep the code base very manageable. Additionally, you have the full power of python at your fingertips so you can do much more than just simply manipulate the data.

I encourage you to try some of these concepts out on your scenarios and see if you can find a way to automate that painful Excel task that hangs over your head every day, week or month.

Good luck!

import pandas as pd
import numpy as np
import glob
 
# filenames
excel_names = ["123.xlsx", "1234.xlsx", "12345.xlsx"]
 
# read them in
excels = [pd.ExcelFile(name) for name in excel_names]
 
# turn them into dataframes
frames = [x.parse(x.sheet_names[0], header=None,index_col=None) for x in excels]
 
# delete the first row for all frames except the first
# i.e. remove the header row -- assumes it's the first
frames[1:] = [df[1:] for df in frames[1:]]
 
# concatenate them..
combined = pd.concat(frames)
 
# write it out
combined.to_excel("c.xlsx", header=False, index=False)