Using Excel with pandas

This post was originally published here

Excel is one of the most popular and widely-used data tools; it's hard to find an organization that doesn't work with it in some way. From analysts, to sales VPs, to CEOs, various professionals use Excel for both quick stats and serious data crunching.

With Excel being so pervasive, data professionals must be familiar with it. You'll also want a tool that can easily read and write Excel files — pandas is perfect for this.

Pandas has excellent methods for reading all kinds of data from Excel files. You can also export your results from pandas back to Excel, if that's preferred by your intended audience. Pandas is great for other routine data analysis tasks, such as:

  • quick Exploratory Data Analysis (EDA)
  • drawing attractive plots
  • feeding data into machine learning tools like scikit-learn
  • building machine learning models on your data
  • taking cleaned and processed data to any number of data tools

Pandas is better at automating data processing tasks than Excel, including processing Excel files.

img-panda-1

In this tutorial, we are going to show you how to work with Excel files in pandas. We will cover the following concepts.

  • setting up your computer with the necessary software
  • reading in data from Excel files into pandas
  • data exploration in pandas
  • visualizing data in pandas using the matplotlib visualization library
  • manipulating and reshaping data in pandas
  • moving data from pandas into Excel

Note that this tutorial does not provide a deep dive into pandas. To explore pandas more, check out our course.

System prerequisites

We will use Python 3 and Jupyter Notebook to demonstrate the code in this tutorial.
In addition to Python and Jupyter Notebook, you will need the following Python modules:

  • matplotlib – data visualization
  • NumPy – numerical data functionality
  • OpenPyXL – read/write Excel 2010 xlsx/xlsm files
  • pandas – data import, clean-up, exploration, and analysis
  • xlrd – read Excel data
  • xlwt – write to Excel
  • XlsxWriter – write to Excel (xlsx) files

There are multiple ways to get set up with all the modules. We cover three of the most common scenarios below.

  • If you have Python installed via Anaconda package manager, you can install the required modules using the command conda install. For example, to install pandas, you would execute the command – conda install pandas.

  • If you already have a regular, non-Anaconda Python installed on the computer, you can install the required modules using pip. Open your command line program and execute command pip install <module name> to install a module. You should replace <module name> with the actual name of the module you are trying to install. For example, to install pandas, you would execute command – pip install pandas.

  • If you don't have Python already installed, you should get it through the Anaconda package manager. Anaconda provides installers for Windows, Mac, and Linux Computers. If you choose the full installer, you will get all the modules you need, along with Python and pandas within a single package. This is the easiest and fastest way to get started.

The data set

In this tutorial, we will use a multi-sheet Excel file we created from Kaggle's IMDB Scores data. You can download the file here.

img-excel-1

Our Excel file has three sheets: ‘1900s,' ‘2000s,' and ‘2010s.' Each sheet has data for movies from those years.

We will use this data set to find the ratings distribution for the movies, visualize movies with highest ratings and net earnings and calculate statistical information about the movies. We will be analyzing and exploring this data using pandas, thus demonstrating pandas capabilities to work with Excel data.

Read data from the Excel file

We need to first import the data from the Excel file into pandas. To do that, we start by importing the pandas module.

import pandas as pd

We then use the pandas' read_excel method to read in data from the Excel file. The easiest way to call this method is to pass the file name. If no sheet name is specified then it will read the first sheet in the index (as shown below).

excel_file = 'movies.xls'
movies = pd.read_excel(excel_file)

Here, the read_excel method read the data from the Excel file into a pandas DataFrame object. Pandas defaults to storing data in DataFrames. We then stored this DataFrame into a variable called movies.

Pandas has a built-in DataFrame.head() method that we can use to easily display the first few rows of our DataFrame. If no argument is passed, it will display first five rows. If a number is passed, it will display the equal number of rows from the top.

movies.head()

Title Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
0 Intolerance: Love's Struggle Throughout the Ages 1916 Drama|History|War NaN USA Not Rated 123 1.33 385907.0 NaN 436 22 9.0 481 691 1 10718 88 69.0 8.0
1 Over the Hill to the Poorhouse 1920 Crime|Drama NaN USA NaN 110 1.33 100000.0 3000000.0 2 2 0.0 4 0 1 5 1 1.0 4.8
2 The Big Parade 1925 Drama|Romance|War NaN USA Not Rated 151 1.33 245000.0 NaN 81 12 6.0 108 226 0 4849 45 48.0 8.3
3 Metropolis 1927 Drama|Sci-Fi German Germany Not Rated 145 1.33 6000000.0 26435.0 136 23 18.0 203 12000 1 111841 413 260.0 8.3
4 Pandora's Box 1929 Crime|Drama|Romance German Germany Not Rated 110 1.33 NaN 9950.0 426 20 3.0 455 926 1 7431 84 71.0 8.0

5 rows × 25 columns

Excel files quite often have multiple sheets and the ability to read a specific sheet or all of them is very important. To make this easy, the pandas read_excel method takes an argument called sheetname that tells pandas which sheet to read in the data from. For this, you can either use the sheet name or the sheet number. Sheet numbers start with zero. If the sheetname argument is not given, it defaults to zero and pandas will import the first sheet.

By default, pandas will automatically assign a numeric index or row label starting with zero. You may want to leave the default index as such if your data doesn't have a column with unique values that can serve as a better index. In case there is a column that you feel would serve as a better index, you can override the default behavior by setting index_col property to a column. It takes a numeric value for setting a single column as index or a list of numeric values for creating a multi-index.

In the below code, we are choosing the first column, ‘Title', as index (index=0) by passing zero to the index_col argument.

movies_sheet1 = pd.read_excel(excel_file, sheetname=0, index_col=0)
movies_sheet1.head()

Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Director Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
Title
Intolerance: Love's Struggle Throughout the Ages 1916 Drama|History|War NaN USA Not Rated 123 1.33 385907.0 NaN D.W. Griffith 436 22 9.0 481 691 1 10718 88 69.0 8.0
Over the Hill to the Poorhouse 1920 Crime|Drama NaN USA NaN 110 1.33 100000.0 3000000.0 Harry F. Millarde 2 2 0.0 4 0 1 5 1 1.0 4.8
The Big Parade 1925 Drama|Romance|War NaN USA Not Rated 151 1.33 245000.0 NaN King Vidor 81 12 6.0 108 226 0 4849 45 48.0 8.3
Metropolis 1927 Drama|Sci-Fi German Germany Not Rated 145 1.33 6000000.0 26435.0 Fritz Lang 136 23 18.0 203 12000 1 111841 413 260.0 8.3
Pandora's Box 1929 Crime|Drama|Romance German Germany Not Rated 110 1.33 NaN 9950.0 Georg Wilhelm Pabst 426 20 3.0 455 926 1 7431 84 71.0 8.0

5 rows × 24 columns

As you noticed above, our Excel data file has three sheets. We already read the first sheet in a DataFrame above. Now, using the same syntax, we will read in rest of the two sheets too.

movies_sheet2 = pd.read_excel(excel_file, sheetname=1, index_col=0)
movies_sheet2.head()

Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Director Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
Title
102 Dalmatians 2000 Adventure|Comedy|Family English USA G 100.0 1.85 85000000.0 66941559.0 Kevin Lima 2000.0 795.0 439.0 4182 372 1 26413 77.0 84.0 4.8
28 Days 2000 Comedy|Drama English USA PG-13 103.0 1.37 43000000.0 37035515.0 Betty Thomas 12000.0 10000.0 664.0 23864 0 1 34597 194.0 116.0 6.0
3 Strikes 2000 Comedy English USA R 82.0 1.85 6000000.0 9821335.0 DJ Pooh 939.0 706.0 585.0 3354 118 1 1415 10.0 22.0 4.0
Aberdeen 2000 Drama English UK NaN 106.0 1.85 6500000.0 64148.0 Hans Petter Moland 844.0 2.0 0.0 846 260 0 2601 35.0 28.0 7.3
All the Pretty Horses 2000 Drama|Romance|Western English USA PG-13 220.0 2.35 57000000.0 15527125.0 Billy Bob Thornton 13000.0 861.0 820.0 15006 652 2 11388 183.0 85.0 5.8

5 rows × 24 columns

movies_sheet3 = pd.read_excel(excel_file, sheetname=2, index_col=0)
movies_sheet3.head()

Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Director Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
Title
127 Hours 2010.0 Adventure|Biography|Drama|Thriller English USA R 94.0 1.85 18000000.0 18329466.0 Danny Boyle 11000.0 642.0 223.0 11984 63000 0.0 279179 440.0 450.0 7.6
3 Backyards 2010.0 Drama English USA R 88.0 NaN 300000.0 NaN Eric Mendelsohn 795.0 659.0 301.0 1884 92 0.0 554 23.0 20.0 5.2
3 2010.0 Comedy|Drama|Romance German Germany Unrated 119.0 2.35 NaN 59774.0 Tom Tykwer 24.0 20.0 9.0 69 2000 0.0 4212 18.0 76.0 6.8
8: The Mormon Proposition 2010.0 Documentary English USA R 80.0 1.78 2500000.0 99851.0 Reed Cowan 191.0 12.0 5.0 210 0 0.0 1138 30.0 28.0 7.1
A Turtle's Tale: Sammy's Adventures 2010.0 Adventure|Animation|Family English France PG 88.0 2.35 NaN NaN Ben Stassen 783.0 749.0 602.0 3874 0 2.0 5385 22.0 56.0 6.1

5 rows × 24 columns

Since all the three sheets have similar data but for different recordsmovies, we will create a single DataFrame from all the three DataFrames we created above. We will use the pandas concat method for this and pass in the names of the three DataFrames we just created and assign the results to a new DataFrame object, movies. By keeping the DataFrame name same as before, we are over-writing the previously created DataFrame.

movies = pd.concat([movies_sheet1, movies_sheet2, movies_sheet3])

We can check if this concatenation by checking the number of rows in the combined DataFrame by calling the method shape on it that will give us the number of rows and columns.

movies.shape

(5042, 24)

Using the ExcelFile class to read multiple sheets

We can also use the ExcelFile class to work with multiple sheets from the same Excel file. We first wrap the Excel file using ExcelFile and then pass it to read_excel method.

xlsx = pd.ExcelFile(excel_file)
xlsx = pd.ExcelFile(excel_file)
movies_sheets = []
for sheet in xlsx.sheet_names:
    movies_sheets.append(xlsx.parse(sheet))
movies = pd.concat(movies_sheets)

If you are reading an Excel file with a lot of sheets and are creating a lot of DataFrames, ExcelFile is more convenient and efficient in comparison to read_excel. With ExcelFile, you only need to pass the Excel file once, and then you can use it to get the DataFrames. When using read_excel, you pass the Excel file every time and hence the file is loaded again for every sheet. This can be a huge performance drag if the Excel file has many sheets with a large number of rows.

Exploring the data

Now that we have read in the movies data set from our Excel file, we can start exploring it using pandas. A pandas DataFrame stores the data in a tabular format, just like the way Excel displays the data in a sheet. Pandas has a lot of built-in methods to explore the DataFrame we created from the Excel file we just read in.

We already introduced the method head in the previous section that displays few rows from the top from the DataFrame. Let's look at few more methods that come in handy while exploring the data set.

We can use the shape method to find out the number of rows and columns for the DataFrame.

movies.shape

(5042, 25)

This tells us our Excel file has 5042 records and 25 columns or observations. This can be useful in reporting the number of records and columns and comparing that with the source data set.

We can use the tail method to view the bottom rows. If no parameter is passed, only the bottom five rows are returned.

movies.tail()

Title Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
1599 War & Peace NaN Drama|History|Romance|War English UK TV-14 NaN 16.00 NaN NaN 1000.0 888.0 502.0 4528 11000 1.0 9277 44.0 10.0 8.2
1600 Wings NaN Comedy|Drama English USA NaN 30.0 1.33 NaN NaN 685.0 511.0 424.0 1884 1000 5.0 7646 56.0 19.0 7.3
1601 Wolf Creek NaN Drama|Horror|Thriller English Australia NaN NaN 2.00 NaN NaN 511.0 457.0 206.0 1617 954 0.0 726 6.0 2.0 7.1
1602 Wuthering Heights NaN Drama|Romance English UK NaN 142.0 NaN NaN NaN 27000.0 698.0 427.0 29196 0 2.0 6053 33.0 9.0 7.7
1603 Yu-Gi-Oh! Duel Monsters NaN Action|Adventure|Animation|Family|Fantasy Japanese Japan NaN 24.0 NaN NaN NaN 0.0 NaN NaN 0 124 0.0 12417 51.0 6.0 7.0

5 rows × 25 columns

In Excel, you're able to sort a sheet based on the values in one or more columns. In pandas, you can do the same thing with the sort_values method. For example, let's sort our movies DataFrame based on the Gross Earnings column.

sorted_by_gross = movies.sort_values(['Gross Earnings'], ascending=False)

Since we have the data sorted by values in a column, we can do few interesting things with it. For example, we can display the top 10 movies by Gross Earnings.

sorted_by_gross["Gross Earnings"].head(10)

1867    760505847.0
1027    658672302.0
1263    652177271.0
610     623279547.0
611     623279547.0
1774    533316061.0
1281    474544677.0
226     460935665.0
1183    458991599.0
618     448130642.0
Name: Gross Earnings, dtype: float64

We can also create a plot for the top 10 movies by Gross Earnings. Pandas makes it easy to visualize your data with plots and charts through matplotlib, a popular data visualization library. With a couple lines of code, you can start plotting. Moreover, matplotlib plots work well inside Jupyter Notebooks since you can displace the plots right under the code.

First, we import the matplotlib module and set matplotlib to display the plots right in the Jupyter Notebook.

import matplotlib.pyplot as plt
%matplotlib inline

We will draw a bar plot where each bar will represent one of the top 10 movies. We can do this by calling the plot method and setting the argument kind to barh. This tells matplotlib to draw a horizontal bar plot.

sorted_by_gross['Gross Earnings'].head(10).plot(kind="barh")
plt.show()

python-pandas-and-excel_28_0

Let's create a histogram of IMDB Scores to check the distribution of IMDB Scores across all movies. Histograms are a good way to visualize the distribution of a data set. We use the plot method on the IMDB Scores series from our movies DataFrame and pass it the argument kind="hist".

movies['IMDB Score'].plot(kind="hist")
plt.show()

python-pandas-and-excel_30_0

This data visualization suggests that most of the IMDB Scores fall between six and eight.

Getting statistical information about the data

Pandas has some very handy methods to look at the statistical data about our data set. For example, we can use the describe method to get a statistical summary of the data set.

movies.describe()

Year Duration Aspect Ratio Budget Gross Earnings Facebook Likes – Director Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
count 4935.000000 5028.000000 4714.000000 4.551000e+03 4.159000e+03 4938.000000 5035.000000 5029.000000 5020.000000 5042.000000 5042.000000 5029.000000 5.042000e+03 5022.000000 4993.000000 5042.000000
mean 2002.470517 107.201074 2.220403 3.975262e+07 4.846841e+07 686.621709 6561.323932 1652.080533 645.009761 9700.959143 7527.457160 1.371446 8.368475e+04 272.770808 140.194272 6.442007
std 12.474599 25.197441 1.385113 2.061149e+08 6.845299e+07 2813.602405 15021.977635 4042.774685 1665.041728 18165.101925 19322.070537 2.013683 1.384940e+05 377.982886 121.601675 1.125189
min 1916.000000 7.000000 1.180000 2.180000e+02 1.620000e+02 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.000000e+00 1.000000 1.000000 1.600000
25% 1999.000000 93.000000 1.850000 6.000000e+06 5.340988e+06 7.000000 614.500000 281.000000 133.000000 1411.250000 0.000000 0.000000 8.599250e+03 65.000000 50.000000 5.800000
50% 2005.000000 103.000000 2.350000 2.000000e+07 2.551750e+07 49.000000 988.000000 595.000000 371.500000 3091.000000 166.000000 1.000000 3.437100e+04 156.000000 110.000000 6.600000
75% 2011.000000 118.000000 2.350000 4.500000e+07 6.230944e+07 194.750000 11000.000000 918.000000 636.000000 13758.750000 3000.000000 2.000000 9.634700e+04 326.000000 195.000000 7.200000
max 2016.000000 511.000000 16.000000 1.221550e+10 7.605058e+08 23000.000000 640000.000000 137000.000000 23000.000000 656730.000000 349000.000000 43.000000 1.689764e+06 5060.000000 813.000000 9.500000

The describe method displays below information for each of the columns.

  • the count or number of values
  • mean
  • standard deviation
  • minimum, maximum
  • 25%, 50%, and 75% quantile

Please note that this information will be calculated only for the numeric values.

We can also use the corresponding method to access this information one at a time. For example, to get the mean of a particular column, you can use the mean method on that column.

movies["Gross Earnings"].mean()

48468407.526809327

Just like mean, there are methods available for each of the statistical information we want to access. You can read about these methods in our free pandas cheat sheet.

Reading files with no header and skipping records

Earlier in this tutorial, we saw some ways to read a particular kind of Excel file that had headers and no rows that needed skipping. Sometimes, the Excel sheet doesn't have any header row. For such instances, you can tell pandas not to consider the first row as header or columns names. And If the Excel sheet's first few rows contain data that should not be read in, you can ask the read_excel method to skip a certain number of rows, starting from the top.

For example, look at the top few rows of this Excel file.
img-excel-no-header-1

This file obviously has no header and first four rows are not actual records and hence should not be read in. We can tell read_excel there is no header by setting argument header to None and we can skip first four rows by setting argument skiprows to four.

movies_skip_rows = pd.read_excel("movies-no-header-skip-rows.xls", header=None, skiprows=4)
movies_skip_rows.head(5)

0 1 2 3 4 5 6 7 8 9 15 16 17 18 19 20 21 22 23 24
0 Metropolis 1927 Drama|Sci-Fi German Germany Not Rated 145 1.33 6000000.0 26435.0 136 23 18.0 203 12000 1 111841 413 260.0 8.3
1 Pandora's Box 1929 Crime|Drama|Romance German Germany Not Rated 110 1.33 NaN 9950.0 426 20 3.0 455 926 1 7431 84 71.0 8.0
2 The Broadway Melody 1929 Musical|Romance English USA Passed 100 1.37 379000.0 2808000.0 77 28 4.0 109 167 8 4546 71 36.0 6.3
3 Hell's Angels 1930 Drama|War English USA Passed 96 1.20 3950000.0 NaN 431 12 4.0 457 279 1 3753 53 35.0 7.8
4 A Farewell to Arms 1932 Drama|Romance|War English USA Unrated 79 1.37 800000.0 NaN 998 164 99.0 1284 213 1 3519 46 42.0 6.6

5 rows × 25 columns

We skipped four rows from the sheet and used none of the rows as the header. Also, notice that one can combine different options in a single read statement. To skip rows at the bottom of the sheet, you can use option skip_footer, which works just like skiprows, the only difference being the rows are counted from the bottom upwards.

The column names in the previous DataFrame are numeric and were allotted as default by the pandas. We can rename the column names to descriptive ones by calling the method columns on the DataFrame and passing the column names as a list.

movies_skip_rows.columns = ['Title', 'Year', 'Genres', 'Language', 'Country', 'Content Rating',
       'Duration', 'Aspect Ratio', 'Budget', 'Gross Earnings', 'Director',
       'Actor 1', 'Actor 2', 'Actor 3', 'Facebook Likes - Director',
       'Facebook Likes - Actor 1', 'Facebook Likes - Actor 2',
       'Facebook Likes - Actor 3', 'Facebook Likes - cast Total',
       'Facebook likes - Movie', 'Facenumber in posters', 'User Votes',
       'Reviews by Users', 'Reviews by Crtiics', 'IMDB Score']
movies_skip_rows.head()

Title Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Facebook Likes – Actor 1 Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score
0 Metropolis 1927 Drama|Sci-Fi German Germany Not Rated 145 1.33 6000000.0 26435.0 136 23 18.0 203 12000 1 111841 413 260.0 8.3
1 Pandora's Box 1929 Crime|Drama|Romance German Germany Not Rated 110 1.33 NaN 9950.0 426 20 3.0 455 926 1 7431 84 71.0 8.0
2 The Broadway Melody 1929 Musical|Romance English USA Passed 100 1.37 379000.0 2808000.0 77 28 4.0 109 167 8 4546 71 36.0 6.3
3 Hell's Angels 1930 Drama|War English USA Passed 96 1.20 3950000.0 NaN 431 12 4.0 457 279 1 3753 53 35.0 7.8
4 A Farewell to Arms 1932 Drama|Romance|War English USA Unrated 79 1.37 800000.0 NaN 998 164 99.0 1284 213 1 3519 46 42.0 6.6

5 rows × 25 columns

Now that we have seen how to read a subset of rows from the Excel file, we can learn how to read a subset of columns.

Reading a subset of columns

Although read_excel defaults to reading and importing all columns, you can choose to import only certain columns. By passing parse_cols=6, we are telling the read_excel method to read only the first columns till index six or first seven columns (the first column being indexed zero).

movies_subset_columns = pd.read_excel(excel_file, parse_cols=6)
movies_subset_columns.head()

Title Year Genres Language Country Content Rating Duration
0 Intolerance: Love's Struggle Throughout the Ages 1916 Drama|History|War NaN USA Not Rated 123
1 Over the Hill to the Poorhouse 1920 Crime|Drama NaN USA NaN 110
2 The Big Parade 1925 Drama|Romance|War NaN USA Not Rated 151
3 Metropolis 1927 Drama|Sci-Fi German Germany Not Rated 145
4 Pandora's Box 1929 Crime|Drama|Romance German Germany Not Rated 110

Alternatively, you can pass in a list of numbers, which will let you import columns at particular indexes.

Applying formulas on the columns

One of the much-used features of Excel is to apply formulas to create new columns from existing column values. In our Excel file, we have Gross Earnings and Budget columns. We can get Net earnings by subtracting Budget from Gross earnings. We could then apply this formula in the Excel file to all the rows. We can do this in pandas also as shown below.

movies["Net Earnings"] = movies["Gross Earnings"] - movies["Budget"]

Above, we used pandas to create a new column called Net Earnings, and populated it with the difference of Gross Earnings and Budget. It's worth noting the difference here in how formulas are treated in Excel versus pandas. In Excel, a formula lives in the cell and updates when the data changes – with Python, the calculations happen and the values are stored – if Gross Earnings for one movie was manually changed, Net Earnings won't be updated.

Let's use the sot_values method to sort the data by the new column we created and visualize the top 10 movies by Net Earnings.

sorted_movies = movies[['Net Earnings']].sort_values(['Net Earnings'], ascending=[False])
sorted_movies.head(10)['Net Earnings'].plot.barh()
plt.show()

python-pandas-and-excel_44_0

Pivot Table in pandas

Advanced Excel users also often use pivot tables. A pivot table summarizes the data of another table by grouping the data on an index and applying operations such as sorting, summing, or averaging. You can use this feature in pandas too.

We need to first identify the column or columns that will serve as the index, and the column(s) on which the summarizing formula will be applied. Let's start small, by choosing Year as the index column and Gross Earnings as the summarization column and creating a separate DataFrame from this data.

movies_subset = movies[['Year', 'Gross Earnings']]
movies_subset.head()

Year Gross Earnings
0 1916.0 NaN
1 1920.0 3000000.0
2 1925.0 NaN
3 1927.0 26435.0
4 1929.0 9950.0

We now call pivot_table on this subset of data. The method pivot_table takes a parameter index. As mentioned, we want to use Year as the index.

earnings_by_year = movies_subset.pivot_table(index=['Year'])
earnings_by_year.head()

Gross Earnings
Year
1916.0 NaN
1920.0 3000000.0
1925.0 NaN
1927.0 26435.0
1929.0 1408975.0

This gave us a pivot table with grouping on Year and summarization on the sum of Gross Earnings. Notice, we didn't need to specify Gross Earnings column explicitly as pandas automatically identified it the values on which summarization should be applied.

We can use this pivot table to create some data visualizations. We can call the plot method on the DataFrame to create a line plot and call the show method to display the plot in the notebook.

earnings_by_year.plot()
plt.show()

python-pandas-and-excel_50_0

We saw how to pivot with a single column as the index. Things will get more interesting if we can use multiple columns. Let's create another DataFrame subset but this time we will choose the columns, Country, Language and Gross Earnings.

movies_subset = movies[['Country', 'Language', 'Gross Earnings']]
movies_subset.head()

Country Language Gross Earnings
0 USA NaN NaN
1 USA NaN 3000000.0
2 USA NaN NaN
3 Germany German 26435.0
4 Germany German 9950.0

We will use columns Country and Language as the index for the pivot table. We will use Gross Earnings as summarization table, however, we do not need to specify this explicitly as we saw earlier.

earnings_by_co_lang = movies_subset.pivot_table(index=['Country', 'Language'])
earnings_by_co_lang.head()

Gross Earnings
Country Language
Afghanistan Dari 1.127331e+06
Argentina Spanish 7.230936e+06
Aruba English 1.007614e+07
Australia Aboriginal 6.165429e+06
Dzongkha 5.052950e+05

Let's visualize this pivot table with a bar plot. Since there are still few hundred records in this pivot table, we will plot just a few of them.

earnings_by_co_lang.head(20).plot(kind='bar', figsize=(20,8))
plt.show()

python-pandas-and-excel_56_0

Exporting the results to Excel

If you're going to be working with colleagues who use Excel, saving Excel files out of pandas is important. You can export or write a pandas DataFrame to an Excel file using pandas to_excel method. Pandas uses the xlwt Python module internally for writing to Excel files. The to_excel method is called on the DataFrame we want to export.We also need to pass a filename to which this DataFrame will be written.

movies.to_excel('output.xlsx')

By default, the index is also saved to the output file. However, sometimes the index doesn't provide any useful information. For example, the movies DataFrame has a numeric auto-increment index, that was not part of the original Excel data.

movies.head()

Title Year Genres Language Country Content Rating Duration Aspect Ratio Budget Gross Earnings Facebook Likes – Actor 2 Facebook Likes – Actor 3 Facebook Likes – cast Total Facebook likes – Movie Facenumber in posters User Votes Reviews by Users Reviews by Crtiics IMDB Score Net Earnings
0 Intolerance: Love's Struggle Throughout the Ages 1916.0 Drama|History|War NaN USA Not Rated 123.0 1.33 385907.0 NaN 22.0 9.0 481 691 1.0 10718 88.0 69.0 8.0 NaN
1 Over the Hill to the Poorhouse 1920.0 Crime|Drama NaN USA NaN 110.0 1.33 100000.0 3000000.0 2.0 0.0 4 0 1.0 5 1.0 1.0 4.8 2900000.0
2 The Big Parade 1925.0 Drama|Romance|War NaN USA Not Rated 151.0 1.33 245000.0 NaN 12.0 6.0 108 226 0.0 4849 45.0 48.0 8.3 NaN
3 Metropolis 1927.0 Drama|Sci-Fi German Germany Not Rated 145.0 1.33 6000000.0 26435.0 23.0 18.0 203 12000 1.0 111841 413.0 260.0 8.3 -5973565.0
4 Pandora's Box 1929.0 Crime|Drama|Romance German Germany Not Rated 110.0 1.33 NaN 9950.0 20.0 3.0 455 926 1.0 7431 84.0 71.0 8.0 NaN

5 rows × 26 columns

You can choose to skip the index by passing along index-False.

movies.to_excel('output.xlsx', index=False)

We need to be able to make our output files look nice before we can send it out to our co-workers. We can use pandas ExcelWriter class along with the XlsxWriter Python module to apply the formatting.

We can do use these advanced output options by creating a ExcelWriter object and use this object to write to the EXcel file.

writer = pd.ExcelWriter('output.xlsx', engine='xlsxwriter')

movies.to_excel(writer, index=False, sheet_name='report')

workbook = writer.book

worksheet = writer.sheets['report']

We can apply customizations by calling add_format on the workbook we are writing to. Here we are setting header format as bold.

header_fmt = workbook.add_format({'bold': True})
worksheet.set_row(0, None, header_fmt)

Finally, we save the output file by calling the method save on the writer object.

writer.save()

As an example, we saved the data with column headers set as bold. And the saved file looks like the image below.

img-excel-output-bold-1

Like this, one can use XlsxWriter to apply various formatting to the output Excel file.

Conclusion

Pandas is not a replacement for Excel. Both tools have their place in the data analysis workflow and can be very great companion tools. As we demonstrated, pandas can do a lot of complex data analysis and manipulations, which depending on your need and expertise, can go beyond what you can achieve if you are just using Excel. 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. Pandas also has excellent methods for reading all kinds of data from Excel files. You can export your results from pandas back to Excel too if that's preferred by your intended audience.

On the other hand, Excel is a such a widely used data tool, it's not a wise to ignore it. Acquiring expertise in both pandas and Excel and making them work together gives you skills that can help you stand out in your organization.

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