Pandas - Load data from Excel file and Display Chart

Pandas - Load data from Excel file and Display Chart

Pandas is a Python package that provides fast data manipulation and analysis of structured data. It has grown in popularity recently with the rise of data analytics and machine learning. The source code for pandas is available on GitHub, but it is more common to install the binary package using something like pip install pandas. A core component of pandas is the dataframe that provides two-dimensional table-like structure to store and manipulate data.

Pandas makes working with structured data easy using the flexible data structures provided. It has become an essential tool for doing practical data analysis in Python. Using Pandas in Jupyter notebooks is a very efficient way to load data and perform some high-level analysis and then visualize the data using Matplotlib.

Under-five mortality rate

I came across a book from Unicef called "state of the world's children" in 1995. This reports on key issues affecting children in various countries around the world and includes a large set of statistics in tabular format, which I found fascinating. Unicef works to improve child welfare throughout the world and continues to produce numerous reports including the state of the word's children. Unicef makes available a large amount of datasets for public consumption at Unicef Datasets. This article uses Under 5 Mortality Rate dataset.

Under-five mortality rate:

is the probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births.


Pandas functions used in this article

  • pandas.ExcelFile
  • pandas.read_excel
  • pandas.shape
  • pandas.head
  • pandas.tail
  • pandas.iloc
  • pandas.drop
  • pandas.columns
  • pandas.rename
  • pandas.sort_values
  • pandas.reset_index
  • pandas.plot


Python modules used in this article

1# import modules
2import matplotlib.pyplot as plt
3import shutil
4import pandas as pd
5import numpy as np
6import requests
7import os


Download the excel file

Use the following function to download any file to a temporary location. It is always better to copy data locally and then work with the local files.

 1def download_file(url):
 2    '''Download a file from url to local temp directory.
 3
 4    Keyword arguments:
 5    url  -- A fully qualified url file path
 6
 7    return: The fully qualified path to the downloaded file
 8    '''
 9    local_dir = '/tmp/data/'
10    if not os.path.exists(local_dir):
11        os.makedirs(local_dir)
12
13    local_file = local_dir + url.split('/')[-1]
14    r = requests.get(url, stream=True)
15    with open(local_file, 'wb') as f:
16        for chunk in r.iter_content(chunk_size=512*1024):
17            if chunk:
18                f.write(chunk)
19    return local_file

This function is used by passing in the url to the Excel file on under-five mortality rate.

1u5mr_url = 'https://data.unicef.org/wp-content/uploads/2020/09/Under-five-mortality-rate_2020.xlsx'
2u5mr_file = download_file(u5mr_url)
3
4print(f"local file = {u5mr_file}")
5# local file = /tmp/data/Under-five-mortality-rate_2020.xlsx


List the worksheets in an excel file

Loading an excel file into a Pandas dataframe is very straight forward with read_excel() and it will default to loading the first worksheet in the Excel file. It is also possible to find out what sheets are in an Excel file with the following code. This shows that the file contains six worksheets and the first one is the country estimates needed.

 1u5mr_xl = pd.ExcelFile(u5mr_file)
 2u5mr_xl.sheet_names
 3
 4
 5# output
 6'''
 7['Country estimates (both sexes)',
 8 'Regional & global (both sexes)',
 9 'Country estimates (Female)',
10 'Regional & global (Female)',
11 'Country estimates (Male)',
12 'Regional & global (Male)']
13 '''


Load excel worksheet into dataframe

First load the first worksheet into a dataframe and examine the data. Examine the data in the dataframe using shape and head. The shape of the data is (601, 73), which is 601 rows of data by 73 columns. The head of the dataframe shows the columns do not have any names and there seems to be a lot of blank rows at the top.

DataFrame.head function returns the first n rows of the dataframe, the default is 5. The first 5 rows appear largely blank except for file information in the first column. The data can be seen to start at row index 13 - when the head function is passed 20 for the first 20 rows.

Displaying all 73 columns can be somewhat unwieldy so the DataFrame.iloc function is used to display the first 20 rows for the first 5 columns. iloc is an integer based indexing mechanism for selecting rows and columns by position. The tail function can be used in a similar way to the head function except to display the last n rows of the dataframe. iloc is used here as well to reduce the number of columns to display.

 1u5mr_df = pd.read_excel(u5mr_file)
 2
 3# OR
 4# u5mr_df = pd.read_excel(u5mr_file, sheet_name = 'Country estimates (both sexes)')
 5
 6print(u5mr_df.shape)
 7"""
 8(601, 73)
 9"""
10
11u5mr_df.head()
12u5mr_df.head(20)
13
14print(u5mr_df.iloc[:20, 0:5])
15"""
16                                           Unnamed: 0    Unnamed: 1  UNICEF GLOBAL DATABASES [data.unicef.org]  Unnamed: 3  Unnamed: 4  
170                                                 NaN           NaN                                        NaN         NaN         NaN  
181                                                 NaN           NaN                                        NaN         NaN         NaN  
192                           Child Mortality Estimates           NaN                                        NaN         NaN         NaN  
203   Country-specific Under-five mortality rate, bo...           NaN                                        NaN         NaN         NaN  
214                                                 NaN           NaN                                        NaN         NaN         NaN  
225   Estimates generated by the UN Inter-agency Gro...           NaN                                        NaN         NaN         NaN  
236              Downloaded from http://data.unicef.org           NaN                                        NaN         NaN         NaN  
247                                                 NaN           NaN                                        NaN         NaN         NaN  
258   Prepared by the Data and Analytics Section; Di...           NaN                                        NaN         NaN         NaN  
269                        Contact us:  data@unicef.org           NaN                                        NaN         NaN         NaN  
2710                                                NaN           NaN                                        NaN         NaN         NaN  
2811                      Last update: 9 September 2020           NaN                                        NaN         NaN         NaN  
2912                                                NaN           NaN                                        NaN         NaN         NaN  
3013                                           ISO.Code  Country.Name                        Uncertainty.Bounds*      1950.5      1951.5  
3114                                                AFG   Afghanistan                                      Lower         NaN         NaN  
3215                                                AFG   Afghanistan                                     Median         NaN         NaN  
3316                                                AFG   Afghanistan                                      Upper         NaN         NaN  
3417                                                ALB       Albania                                      Lower         NaN         NaN  
3518                                                ALB       Albania                                     Median         NaN         NaN  
3619                                                ALB       Albania                                      Upper         NaN         NaN  
37"""
38
39
40u5mr_df.tail()
41print(u5mr_df.iloc[-5:, 0:5])
42"""
43    Unnamed: 0                                         Unnamed: 1   UNICEF GLOBAL DATABASES [data.unicef.org]  Unnamed: 3  Unnamed: 4  
44596        ZWE                                           Zimbabwe                                       Lower         NaN         NaN  
45597        ZWE                                           Zimbabwe                                      Median         NaN         NaN  
46598        ZWE                                           Zimbabwe                                       Upper         NaN         NaN  
47599        NaN                                                NaN                                         NaN         NaN         NaN  
48600        NaN  * Lower and Upper refer to the lower bound and...                                         NaN         NaN         NaN  
49"""
50

It can be seen from the output above that the header row for the countries start at index 13. The data is reloaded from the excel file with an additional parameter specifying the header row. This could also be done using the skiprows. It also shows that the last two rows are there to contain a footnote.

Reload the excel file with the following code and review the data.

 1u5mr_df = pd.read_excel(
 2    u5mr_file,
 3    sheet_name = 'Country estimates (both sexes)',
 4    header = 14)
 5
 6print(u5mr_df.shape)
 7"""
 8(587, 73)
 9"""
10
11print(u5mr_df.iloc[:10, 0:8])
12"""
13  ISO.Code Country.Name Uncertainty.Bounds*  1950.5  1951.5  1952.5  1953.5      1954.5  
140      AFG  Afghanistan               Lower     NaN     NaN     NaN     NaN         NaN  
151      AFG  Afghanistan              Median     NaN     NaN     NaN     NaN         NaN  
162      AFG  Afghanistan               Upper     NaN     NaN     NaN     NaN         NaN  
173      ALB      Albania               Lower     NaN     NaN     NaN     NaN         NaN  
184      ALB      Albania              Median     NaN     NaN     NaN     NaN         NaN  
195      ALB      Albania               Upper     NaN     NaN     NaN     NaN         NaN  
206      DZA      Algeria               Lower     NaN     NaN     NaN     NaN  181.095341  
217      DZA      Algeria              Median     NaN     NaN     NaN     NaN  242.618134  
228      DZA      Algeria               Upper     NaN     NaN     NaN     NaN  323.057628  
239      AND      Andorra               Lower     NaN     NaN     NaN     NaN         NaN  
24"""
25
26# Review columns on the right
27print(u5mr_df.iloc[:10, -7:])
28"""
29      2013.5     2014.5     2015.5     2016.5     2017.5     2018.5     2019.5
300  68.481212  64.526394  60.716842  57.068054  53.744798  50.640922  47.442070
311  76.830462  73.580267  70.444802  67.572190  64.940759  62.541196  60.269399
322  86.077559  83.378062  80.863431  78.646207  76.832873  75.405564  74.616041
333  10.072106   9.549495   9.225420   9.075629   9.044506   9.067390   9.068154
344  10.430484   9.896397   9.565251   9.419110   9.418052   9.525133   9.682407
355  10.821932  10.272281   9.936220   9.795035   9.826251  10.020056  10.348964
366  25.265117  24.944603  24.613434  24.245765  23.801969  23.262592  22.513380
377  25.784460  25.478560  25.177142  24.792098  24.319482  23.805926  23.256168
388  26.310524  26.023294  25.746094  25.339485  24.843878  24.345820  24.025946
399   1.566198   1.384200   1.232419   1.098862   0.985541   0.910653   0.847791
40"""
41


Rename the columns to contain the Year

Drop the last two rows that are there to include the footnote.

 1# Drop the last two rows
 2print(u5mr_df.shape)
 3"""
 4(587, 73)
 5"""
 6
 7u5mr_df.drop(u5mr_df.tail(2).index, inplace = True)
 8
 9print(u5mr_df.shape)
10"""
11(585, 73)
12"""
13
14u5mr_df.tail()
15print(u5mr_df.iloc[-5:, 0:7])
16"""
17    ISO.Code Country.Name Uncertainty.Bounds*  1950.5  1951.5  1952.5      1953.5  
18580      ZMB       Zambia              Median     NaN     NaN     NaN  234.418232  
19581      ZMB       Zambia               Upper     NaN     NaN     NaN  308.531403  
20582      ZWE     Zimbabwe               Lower     NaN     NaN     NaN         NaN  
21583      ZWE     Zimbabwe              Median     NaN     NaN     NaN         NaN  
22584      ZWE     Zimbabwe               Upper     NaN     NaN     NaN         NaN  
23"""

The year headings contain 0.5 such as 2019.5 probably to indicate a mean value for the entire year. Use columns field of the dataframe to rename these to just the full year. u5mr_df.columns is a list of all the columns in the dataframe and a list comprehension is used to remove the ".5" if it is there.

The asterisk (*) is also removed from the 'Uncertainty.Bounds*' column name.

 1print(u5mr_df.columns)
 2
 3"""
 4Index(['ISO.Code', 'Country.Name', 'Uncertainty.Bounds*', '1950.5', '1951.5',
 5       '1952.5', '1953.5', '1954.5', '1955.5', '1956.5', '1957.5', '1958.5',
 6       '1959.5', '1960.5', '1961.5', '1962.5', '1963.5', '1964.5', '1965.5',
 7       '1966.5', '1967.5', '1968.5', '1969.5', '1970.5', '1971.5', '1972.5',
 8       '1973.5', '1974.5', '1975.5', '1976.5', '1977.5', '1978.5', '1979.5',
 9       '1980.5', '1981.5', '1982.5', '1983.5', '1984.5', '1985.5', '1986.5',
10       '1987.5', '1988.5', '1989.5', '1990.5', '1991.5', '1992.5', '1993.5',
11       '1994.5', '1995.5', '1996.5', '1997.5', '1998.5', '1999.5', '2000.5',
12       '2001.5', '2002.5', '2003.5', '2004.5', '2005.5', '2006.5', '2007.5',
13       '2008.5', '2009.5', '2010.5', '2011.5', '2012.5', '2013.5', '2014.5',
14       '2015.5', '2016.5', '2017.5', '2018.5', '2019.5'],
15      dtype='object')
16"""
17
18u5mr_df.columns = [x[:-2] if x.endswith('.5') else x for x in u5mr_df.columns]
19
20u5mr_df = u5mr_df.rename(columns={'Uncertainty.Bounds*': 'Uncertainty.Bounds'})
21
22print(u5mr_df.columns)
23"""
24Index(['ISO.Code', 'Country.Name', 'Uncertainty.Bounds', '1950', '1951',
25       '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960',
26       '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969',
27       '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978',
28       '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987',
29       '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996',
30       '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',
31       '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014',
32       '2015', '2016', '2017', '2018', '2019'],
33      dtype='object')
34"""


Filter to the Median values

The data has three values for each country the Lower and Upper bounds and the Median. Create a copy of the dataframe with just the Median values, so there is only one number for each country.

 1u5mr_med_df = u5mr_df[u5mr_df['Uncertainty.Bounds'] == 'Median']
 2print(u5mr_med_df.shape)
 3"""
 4(195, 73)
 5"""
 6
 7u5mr_med_df.head()
 8print(u5mr_med_df.iloc[:10, :10])
 9"""
10   ISO.Code         Country.Name Uncertainty.Bounds        1950        1951        1952        1953        1954        1955        1956  
111       AFG          Afghanistan             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
124       ALB              Albania             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
137       DZA              Algeria             Median         NaN         NaN         NaN         NaN  242.618134  242.066662  241.601453  
1410      AND              Andorra             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
1513      AGO               Angola             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
1616      ATG  Antigua and Barbuda             Median  119.066130  116.624640  114.074405  111.406742  108.602334  105.661011  102.585410  
1719      ARG            Argentina             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
1822      ARM              Armenia             Median         NaN         NaN         NaN         NaN         NaN         NaN         NaN  
1925      AUS            Australia             Median   31.584087   30.966060   30.261701   29.496194   28.711058   27.961205   27.265216  
2028      AUT              Austria             Median   83.482005   73.820239   66.122978   60.263643   56.225511   53.524855   51.536432  
21"""


Get data for the top 10 countries in the latest year

The data is sorted by the values in the last column and the number of rows reduced to ten to reduce the data to the top 10 countries.

 1u5mr_med_df.sort_values(by=u5mr_df.columns[-1], ascending=False).head(10)
 2
 3highest_df = u5mr_med_df.sort_values(by=u5mr_df.columns[-1], ascending=False).head(10)
 4print(highest_df.iloc[:, [0,1,-5,-4,-3,-2,-1]])
 5"""
 6    ISO.Code              Country.Name        2015        2016        2017        2018        2019  
 7376      NGA                   Nigeria  126.833543  125.040170  122.798947  120.037728  117.202078  
 8481      SOM                   Somalia  132.720296  128.428442  124.393442  120.326222  116.972096  
 9100      TCD                      Chad  129.406257  125.604878  121.508350  117.664883  113.790418  
1097       CAF  Central African Republic  126.714463  121.593307  117.470988  114.222380  110.053912  
11466      SLE              Sierra Leone  138.342774  123.232842  118.222181  113.544491  109.236528  
12214      GIN                    Guinea  107.852191  105.241523  103.226528  101.095543   98.802973  
13487      SSD               South Sudan   96.229299   96.229299   96.229299   96.229299   96.229299  
14316      MLI                      Mali  108.586504  104.766458  101.062422   97.410316   94.035418  
1555       BEN                     Benin   99.592931   97.416395   95.133079   92.773521   90.286429  
1679       BFA              Burkina Faso  100.671924   97.034768   93.862909   90.682138   87.542426  
17"""


Create a chart for the top 10 countries in the latest year

Create a line chart using Matplotlib to show the changes in the under-five mortality rate over time. The data needs to be transposed and tidyied up a bit in order to create a line chart using year as the x axis values.

The following steps are done to prepare the data for the chart.

  1. Transpose the data so year can be used as the x-axis
  2. Set the Country.Name as the heading for the columns
  3. Rename the Country.Name column to Year
  4. Drop the rows that do not contain u5mr data
 1# Need to transpose the dataframe to use year as the x-axis
 2top_10_df = u5mr_med_df.sort_values(by=u5mr_df.columns[-1], ascending=False).head(10).T
 3top_10_df.reset_index(drop=False,inplace=True)
 4print(f"shape = {top_10_df.shape}")
 5top_10_df.head()
 6
 7# Set the Country.Name as the heading for the columns
 8top_10_df.columns = top_10_df.iloc[np.where(top_10_df['index'] == 'Country.Name')[0][0]]
 9print(f"shape = {top_10_df.shape}")
10top_10_df.head()
11
12# Rename the Country.Name column to Year
13top_10_df = top_10_df.rename(columns={'Country.Name': 'Year'})
14print(f"shape = {top_10_df.shape}")
15top_10_df.head()
16
17# Drop the rows that do not contain u5mr data
18top_10_df = top_10_df[top_10_df['Year'].isin(['ISO.Code', 'Country.Name', 'Uncertainty.Bounds']) == False]
19top_10_df.reset_index(drop = True, inplace = True)
20print(f"shape = {top_10_df.shape}")
21top_10_df.head()
22
23print(top_10_df.tail())
24"""
251   Year  Nigeria  Somalia     Chad Central African Republic Sierra Leone   Guinea South Sudan     Mali    Benin Burkina Faso  
2665  2015  126.834   132.72  129.406                  126.714      138.343  107.852     96.2293  108.587  99.5929      100.672  
2766  2016   125.04  128.428  125.605                  121.593      123.233  105.242     96.2293  104.766  97.4164      97.0348  
2867  2017  122.799  124.393  121.508                  117.471      118.222  103.227     96.2293  101.062  95.1331      93.8629  
2968  2018  120.038  120.326  117.665                  114.222      113.544  101.096     96.2293  97.4103  92.7735      90.6821  
3069  2019  117.202  116.972   113.79                  110.054      109.237   98.803     96.2293  94.0354  90.2864      87.5424  
31"""


Create a chart for countries with highest U5MR

The code below creates the line chart of the changes to under-five mortality rate in the countries with the current highest rates.

 1current_year = u5mr_df.columns[-1]
 2title = f'Changes in Under 5 Mortality Rate\nfor countries with the highest in {u5mr_df.columns[-1]}'
 3
 4fig, ax1 = plt.subplots(figsize=(10,7), facecolor=plt.cm.Blues(.2))
 5ax1.set_facecolor(plt.cm.Blues(.2))
 6
 7fig.suptitle(title, fontsize=18,  fontweight='bold')
 8
 9top_10_df.plot(kind = "line",
10               x = "Year",
11               y = top_10_df.columns[1:],
12               ax = ax1)
13
14ax1.legend(bbox_to_anchor = (0.5, -0.1),
15           loc='upper center',
16           ncol = 3,
17           frameon = False,
18           fontsize = 'medium')
19
20ax1.set_ylabel('Under-five mortality rate', fontsize=14)
21ax1.set_xlabel('Year', fontsize=14)
22
23# Hide the right and top spines
24ax1.spines['right'].set_visible(False)
25ax1.spines['top'].set_visible(False)
26
27plt.show()
28

Changes in Under 5 Mortality Rate for countries with the highest rates



Create a chart for countries with lowest U5MR

A similar chart was created by reversing the sort order of the mortality rates in the latest year after performing the data clean. These two functions were created to make the creation of the charts more consistent and reproducible.

Function to extract the mortality rates for the top countries.

 1def get_top_countries(data_df, lowest = True, num = 10):
 2    '''Extract the mortality rates for the top number of countries
 3    based on rates in the latest year
 4
 5    Keyword arguments:
 6    data_df  -- dateframe of all the mortality rates for all the countries
 7    lowest   -- boolean flag to either contries withe the lowest rate
 8                or highest rate (default is True)
 9    num.     -- number of countries to return (default is 10)
10
11    return: dataframe that has been transposed and filtered to the top n counbtries
12    '''
13    # Need to transpose the dataframe to use year as the x-axis
14    df = data_df.sort_values(by=data_df.columns[-1], ascending=lowest).head(10).T
15    df.reset_index(drop=False,inplace=True)
16
17    # Set the Country.Name as the heading for the columns
18    df.columns = df.iloc[np.where(df['index'] == 'Country.Name')[0][0]]
19
20    # Rename the Country.Name column to Year
21    df = df.rename(columns={'Country.Name': 'Year'})
22
23    # Drop the rows that do not contain u5mr data
24    df = (df[df['Year']
25             .isin(['ISO.Code',
26                    'Country.Name',
27                    'Uncertainty.Bounds']) == False]
28        )
29    df.reset_index(drop = True, inplace = True)
30
31    return df

Function to create the chart of the mortality rates over time.

 1def create_line_chart(df, title):  
 2    '''Create a line chart of the mortality rates over time.
 3    Year is required to be in the first column.
 4
 5    Keyword arguments:
 6    df     -- dataframe of the the countries to render in the chart.
 7    title  -- text to display for the title of the chart
 8
 9    return: figure of the chart
10    '''
11    fig, ax1 = plt.subplots(figsize=(10,7), facecolor=plt.cm.Blues(.2))
12    ax1.set_facecolor(plt.cm.Blues(.2))
13
14    fig.suptitle(title, fontsize=18,  fontweight='bold')
15
16    df.plot(kind = "line",
17            x = "Year",
18            y = df.columns[1:],
19            ax = ax1)
20
21    ax1.legend(bbox_to_anchor = (0.5, -0.1),
22               loc='upper center',
23               ncol = 3,
24               frameon = False,
25               fontsize = 'medium')
26
27    ax1.set_ylabel('Under-five mortality rate', fontsize=14)
28    ax1.set_xlabel('Year', fontsize=14)
29
30    # Hide the right and top spines
31    ax1.spines['right'].set_visible(False)
32    ax1.spines['top'].set_visible(False)
33
34    return fig
35

Changes in Under 5 Mortality Rate for countries with the lowest rates



Conclusion

Under-Five Mortality Rate

Under-five mortality rate is one of the key development metrics for countries. The results of the current countries with highest continue to decrease year over year. However, some countries such as South Sudan and Somalia seem to have levelled off in recent years. These top ten countries all have U5MR close to 100, that is the expectation that 100 out of every 1,000 live births will die before the age of 5-years of age. This means one in ten people die before the age of five - this is alarming.

All of these ten countries have had high Under-Five Mortality Rate for the last fifty years and they are all improving. The highest rate in this chart is for Mali in 1963 when the Under-Five Mortality Rate was 420. This is an incomprehensible number implying that four out of every ten children are likely to die before the age of 5.

It can be seen that all but one of the top countries with the lowest Under-Five Mortality Rate started at less than 100 in 1950.

Pandas

Pandas is great for loading and manipulating structured data. It is very easy to load data from Excel into a dataframe. Dataframe is also great for filtering, sorting and transposing data as well as creating charts from the data. Pandas can be used in a Jupyter Notebook for interactive exploration of data so that a data processing pipeline can be quickly developed. The final methods can then be used in a python script to repeatedly process similar files and produce consistent output.