import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplotsWalkthroughs and Exercises for Data Analysis in Python
Intro: Foundations of Data Analysis with Python
Walkthrough #1: Setting Up the Python Environment
If you haven’t already installed Python, Jupyter, and the necessary packages, there are instructions on the course repo in the README to do so here.
If you aren’t able to do this on your machine, you may want to check out Google Colab. It’s a free service that allows you to run Jupyter notebooks in the cloud.
# For plotly to load directly in Jupyter notebook
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)Exercise #1: Setting Up the Python Environment
By completing this exercise, you will be able to
- Import necessary Python packages
- Check for successful package loading
Follow the instructions above in Walkthrough #1 to check for correct installation of necessary packages. We’ll wait a few minutes to make sure as many of you are set up as possible. Please give a thumbs up in the pulse check if you are ready to move on.
Module 1: Data Wrangling with Pandas
Walkthrough #2: Loading and Inspecting Data with Pandas
Import data from a CSV or from an Excel file
# Load the data from a CSV file
economies = pd.read_csv("economies.csv")
# Or load the data from an Excel file
economies = pd.read_excel("economies.xlsx")Perform an initial exploration of the data
# Display the first few rows of the DataFrame
economies.head() code country year gdp_percapita gross_savings inflation_rate \
0 ABW Aruba 2010 24087.950 13.255 2.078
1 ABW Aruba 2015 27126.620 21.411 0.475
2 ABW Aruba 2020 21832.920 -7.521 -1.338
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_group
0 NaN 10.600 NaN NaN High income
1 NaN 7.298 NaN NaN High income
2 NaN 13.997 NaN NaN High income
3 30.269 NaN 9.768 32.285 Low income
4 18.427 NaN -11.585 15.309 Low income # Display the information about the DataFrame
economies.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 561 entries, 0 to 560
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 code 561 non-null object
1 country 561 non-null object
2 year 561 non-null int64
3 gdp_percapita 558 non-null float64
4 gross_savings 490 non-null float64
5 inflation_rate 555 non-null float64
6 total_investment 490 non-null float64
7 unemployment_rate 312 non-null float64
8 exports 509 non-null float64
9 imports 506 non-null float64
10 income_group 561 non-null object
dtypes: float64(7), int64(1), object(3)
memory usage: 48.3+ KB# Display summary statistics of the DataFrame
economies.describe() year gdp_percapita gross_savings inflation_rate \
count 561.000000 558.000000 490.000000 555.000000
mean 2015.000000 13447.838281 20.641665 9.762438
std 4.086126 18481.107981 10.813159 103.013164
min 2010.000000 231.549000 -10.331000 -3.900000
25% 2010.000000 1842.815000 14.129000 0.731000
50% 2015.000000 5049.830000 20.536000 2.507000
75% 2020.000000 16509.697500 26.819750 5.406000
max 2020.000000 116921.110000 59.699000 2355.150000
total_investment unemployment_rate exports imports
count 490.000000 312.000000 509.000000 506.000000
mean 25.348976 8.894619 -0.844275 0.813121
std 23.546022 5.605188 17.817279 15.644724
min 0.521000 0.900000 -80.939000 -59.381000
25% 18.449250 5.252250 -8.528000 -8.253000
50% 22.808000 7.400000 1.000000 1.334000
75% 27.644750 10.772000 8.033000 9.348000
max 363.411000 32.050000 159.103000 84.555000 # Check for missing data
economies.isnull().sum()code 0
country 0
year 0
gdp_percapita 3
gross_savings 71
inflation_rate 6
total_investment 71
unemployment_rate 249
exports 52
imports 55
income_group 0
dtype: int64# Check data types
economies.dtypescode object
country object
year int64
gdp_percapita float64
gross_savings float64
inflation_rate float64
total_investment float64
unemployment_rate float64
exports float64
imports float64
income_group object
dtype: objectExercise #2: Loading and Inspecting Data with Pandas
By completing this exercise, you will be able to use pandas to
- Import data from a CSV or from an Excel file
- Perform an initial exploration of the data
# Load the populations data from an Excel file
populations = pd.read_excel("populations.xlsx")
# Inspection methods for populations DataFrame
populations.head() country_code country year fertility_rate life_expectancy size \
0 ABW Aruba 2010 1.941 75.404 100341
1 ABW Aruba 2015 1.972 75.683 104257
2 ABW Aruba 2020 1.325 75.723 106585
3 AFG Afghanistan 2010 6.099 60.851 28189672
4 AFG Afghanistan 2015 5.405 62.659 33753499
official_state_name sovereignty continent \
0 Aruba Netherlands North America
1 Aruba Netherlands North America
2 Aruba Netherlands North America
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
region
0 Caribbean
1 Caribbean
2 Caribbean
3 Southern and Central Asia
4 Southern and Central Asia populations.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 645 entries, 0 to 644
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 country_code 645 non-null object
1 country 645 non-null object
2 year 645 non-null int64
3 fertility_rate 627 non-null float64
4 life_expectancy 623 non-null float64
5 size 645 non-null int64
6 official_state_name 645 non-null object
7 sovereignty 645 non-null object
8 continent 645 non-null object
9 region 645 non-null object
dtypes: float64(2), int64(2), object(6)
memory usage: 50.5+ KBpopulations.describe() year fertility_rate life_expectancy size
count 645.000000 627.000000 623.000000 6.450000e+02
mean 2015.000000 2.727907 71.553996 3.429149e+07
std 4.085651 1.386750 8.118422 1.346457e+08
min 2010.000000 0.837000 45.596000 1.024100e+04
25% 2010.000000 1.670000 65.742000 7.550310e+05
50% 2015.000000 2.216000 73.004000 6.292731e+06
75% 2020.000000 3.537500 77.720695 2.301265e+07
max 2020.000000 7.485000 85.497561 1.411100e+09# Checking for missing data and data types for populations DataFrame
populations.isnull().sum()country_code 0
country 0
year 0
fertility_rate 18
life_expectancy 22
size 0
official_state_name 0
sovereignty 0
continent 0
region 0
dtype: int64populations.dtypescountry_code object
country object
year int64
fertility_rate float64
life_expectancy float64
size int64
official_state_name object
sovereignty object
continent object
region object
dtype: objectWalkthrough #3: Cleaning and Preparing Data with Pandas
Handle missing data
Remove rows
# Remove rows with any missing values
economies_cleaned_any = economies.dropna(how='any')
economies_cleaned_any code country year gdp_percapita gross_savings inflation_rate \
9 ALB Albania 2010 4097.83 20.023 3.615
10 ALB Albania 2015 3953.61 15.804 1.868
11 ALB Albania 2020 5286.68 13.255 1.603
15 ARG Argentina 2010 10412.97 17.323 10.461
17 ARG Argentina 2020 8554.64 17.798 42.015
.. ... ... ... ... ... ...
541 VNM Vietnam 2015 2582.39 26.444 0.631
542 VNM Vietnam 2020 3498.98 28.603 3.222
552 ZAF South Africa 2010 7311.74 18.012 4.264
553 ZAF South Africa 2015 5731.73 16.300 4.575
554 ZAF South Africa 2020 5067.15 14.602 3.268
total_investment unemployment_rate exports imports \
9 31.318 14.000 10.473 -9.316
10 26.237 17.100 5.272 0.076
11 22.845 12.500 -28.951 -21.446
15 17.706 7.750 13.701 39.414
17 16.845 11.364 -13.124 -10.722
.. ... ... ... ...
541 27.339 2.330 9.713 15.426
542 26.444 3.300 2.822 2.948
552 19.513 24.875 7.718 10.794
553 20.918 25.350 2.925 5.443
554 12.426 29.175 -10.280 -16.615
income_group
9 Upper middle income
10 Upper middle income
11 Upper middle income
15 Upper middle income
17 Upper middle income
.. ...
541 Lower middle income
542 Lower middle income
552 Upper middle income
553 Upper middle income
554 Upper middle income
[282 rows x 11 columns]# Remove rows only if all values are missing
economies_cleaned_all = economies.dropna(how='all')
economies_cleaned_all code country year gdp_percapita gross_savings inflation_rate \
0 ABW Aruba 2010 24087.950 13.255 2.078
1 ABW Aruba 2015 27126.620 21.411 0.475
2 ABW Aruba 2020 21832.920 -7.521 -1.338
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
.. ... ... ... ... ... ...
556 ZMB Zambia 2015 1310.460 40.103 10.107
557 ZMB Zambia 2020 981.311 36.030 16.350
558 ZWE Zimbabwe 2010 975.851 NaN 3.045
559 ZWE Zimbabwe 2015 1425.010 NaN -2.410
560 ZWE Zimbabwe 2020 1385.040 NaN 557.210
total_investment unemployment_rate exports imports \
0 NaN 10.600 NaN NaN
1 NaN 7.298 NaN NaN
2 NaN 13.997 NaN NaN
3 30.269 NaN 9.768 32.285
4 18.427 NaN -11.585 15.309
.. ... ... ... ...
556 42.791 NaN -11.407 0.696
557 34.514 NaN 1.143 2.635
558 NaN NaN NaN NaN
559 NaN NaN NaN NaN
560 NaN NaN NaN NaN
income_group
0 High income
1 High income
2 High income
3 Low income
4 Low income
.. ...
556 Lower middle income
557 Lower middle income
558 Lower middle income
559 Lower middle income
560 Lower middle income
[561 rows x 11 columns]# Remove rows with missing values in specific columns
economies_cleaned_subset = economies.dropna(subset=['exports', 'imports'])
economies_cleaned_subset code country year gdp_percapita gross_savings inflation_rate \
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
5 AFG Afghanistan 2020 580.817 27.132 5.607
6 AGO Angola 2010 3641.440 34.833 14.480
7 AGO Angola 2015 4354.920 28.491 9.159
.. ... ... ... ... ... ...
553 ZAF South Africa 2015 5731.730 16.300 4.575
554 ZAF South Africa 2020 5067.150 14.602 3.268
555 ZMB Zambia 2010 1456.050 37.405 8.500
556 ZMB Zambia 2015 1310.460 40.103 10.107
557 ZMB Zambia 2020 981.311 36.030 16.350
total_investment unemployment_rate exports imports \
3 30.269 NaN 9.768 32.285
4 18.427 NaN -11.585 15.309
5 16.420 NaN -10.424 2.892
6 28.197 NaN -3.266 -21.656
7 34.202 NaN 6.721 -19.515
.. ... ... ... ...
553 20.918 25.350 2.925 5.443
554 12.426 29.175 -10.280 -16.615
555 29.878 NaN 19.476 32.492
556 42.791 NaN -11.407 0.696
557 34.514 NaN 1.143 2.635
income_group
3 Low income
4 Low income
5 Low income
6 Lower middle income
7 Lower middle income
.. ...
553 Upper middle income
554 Upper middle income
555 Lower middle income
556 Lower middle income
557 Lower middle income
[506 rows x 11 columns]Remove columns
# Remove columns with any missing values
economies_no_missing_columns = economies.dropna(axis=1)
# Display the DataFrame after removing columns with missing values
economies_no_missing_columns.head() code country year income_group
0 ABW Aruba 2010 High income
1 ABW Aruba 2015 High income
2 ABW Aruba 2020 High income
3 AFG Afghanistan 2010 Low income
4 AFG Afghanistan 2015 Low incomeReplace missing values with specific value
# Replace missing values with a specific value (e.g., 0 for numerical columns, 'Unknown' for categorical columns)
economies_fill_value = economies.fillna({
'gdp_percapita': 0,
'gross_savings': 0,
'inflation_rate': 0,
'total_investment': 0,
'unemployment_rate': 0,
'exports': 0,
'imports': 0,
'income_group': 'Unknown'
})
# Display the DataFrame after replacing missing values with specific values
economies_fill_value.head() code country year gdp_percapita gross_savings inflation_rate \
0 ABW Aruba 2010 24087.950 13.255 2.078
1 ABW Aruba 2015 27126.620 21.411 0.475
2 ABW Aruba 2020 21832.920 -7.521 -1.338
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_group
0 0.000 10.600 0.000 0.000 High income
1 0.000 7.298 0.000 0.000 High income
2 0.000 13.997 0.000 0.000 High income
3 30.269 0.000 9.768 32.285 Low income
4 18.427 0.000 -11.585 15.309 Low income This can be extended to replace missing values with the mean, median, or mode of the column too.
Convert a column to a different data type
# Change year to be a string instead of an integer
economies_char_year = economies.astype({'year': 'str'})
# Display the information on the DataFrame with year as a string
economies_char_year.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 561 entries, 0 to 560
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 code 561 non-null object
1 country 561 non-null object
2 year 561 non-null object
3 gdp_percapita 558 non-null float64
4 gross_savings 490 non-null float64
5 inflation_rate 555 non-null float64
6 total_investment 490 non-null float64
7 unemployment_rate 312 non-null float64
8 exports 509 non-null float64
9 imports 506 non-null float64
10 income_group 561 non-null object
dtypes: float64(7), object(4)
memory usage: 48.3+ KB# Change the year of string type back to integer
economies_int_year = economies_char_year.astype({'year': 'int'})
# Display the information on the DataFrame with year as a string
economies_int_year.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 561 entries, 0 to 560
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 code 561 non-null object
1 country 561 non-null object
2 year 561 non-null int64
3 gdp_percapita 558 non-null float64
4 gross_savings 490 non-null float64
5 inflation_rate 555 non-null float64
6 total_investment 490 non-null float64
7 unemployment_rate 312 non-null float64
8 exports 509 non-null float64
9 imports 506 non-null float64
10 income_group 561 non-null object
dtypes: float64(7), int64(1), object(3)
memory usage: 48.3+ KBRename a column
# Rename the 'income_group' column to 'income_category'
economies_renamed = economies.rename(columns={'income_group': 'income_category'})
economies_renamed.head() code country year gdp_percapita gross_savings inflation_rate \
0 ABW Aruba 2010 24087.950 13.255 2.078
1 ABW Aruba 2015 27126.620 21.411 0.475
2 ABW Aruba 2020 21832.920 -7.521 -1.338
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_category
0 NaN 10.600 NaN NaN High income
1 NaN 7.298 NaN NaN High income
2 NaN 13.997 NaN NaN High income
3 30.269 NaN 9.768 32.285 Low income
4 18.427 NaN -11.585 15.309 Low income Changing a DataFrame’s index
Set the index
# Set unique combinations of 'code' and 'year' as the index
economies_indexed = economies.set_index(['code', 'year'])
economies_indexed.head() country gdp_percapita gross_savings inflation_rate \
code year
ABW 2010 Aruba 24087.950 13.255 2.078
2015 Aruba 27126.620 21.411 0.475
2020 Aruba 21832.920 -7.521 -1.338
AFG 2010 Afghanistan 631.490 59.699 2.179
2015 Afghanistan 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_group
code year
ABW 2010 NaN 10.600 NaN NaN High income
2015 NaN 7.298 NaN NaN High income
2020 NaN 13.997 NaN NaN High income
AFG 2010 30.269 NaN 9.768 32.285 Low income
2015 18.427 NaN -11.585 15.309 Low income Reset the index
# Reset the index
economies_reset = economies_indexed.reset_index()
economies_reset.head() code year country gdp_percapita gross_savings inflation_rate \
0 ABW 2010 Aruba 24087.950 13.255 2.078
1 ABW 2015 Aruba 27126.620 21.411 0.475
2 ABW 2020 Aruba 21832.920 -7.521 -1.338
3 AFG 2010 Afghanistan 631.490 59.699 2.179
4 AFG 2015 Afghanistan 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_group
0 NaN 10.600 NaN NaN High income
1 NaN 7.298 NaN NaN High income
2 NaN 13.997 NaN NaN High income
3 30.269 NaN 9.768 32.285 Low income
4 18.427 NaN -11.585 15.309 Low income Filtering rows based on conditions
Conditions on a single column
# Filter rows where 'gdp_percapita' is greater than 20,000
economies_high_gdp = economies[economies['gdp_percapita'] > 20000]
economies_high_gdp.head() code country year gdp_percapita gross_savings \
0 ABW Aruba 2010 24087.95 13.255
1 ABW Aruba 2015 27126.62 21.411
2 ABW Aruba 2020 21832.92 -7.521
12 ARE United Arab Emirates 2010 35064.26 31.330
13 ARE United Arab Emirates 2015 37380.57 30.540
inflation_rate total_investment unemployment_rate exports imports \
0 2.078 NaN 10.600 NaN NaN
1 0.475 NaN 7.298 NaN NaN
2 -1.338 NaN 13.997 NaN NaN
12 0.878 27.121 NaN 7.540 0.405
13 4.070 25.639 NaN 3.055 2.488
income_group
0 High income
1 High income
2 High income
12 High income
13 High income # Filter rows where 'income_group' is 'High income'
economies_high_income = economies[economies['income_group'] == 'High income']
economies_high_income.head() code country year gdp_percapita gross_savings \
0 ABW Aruba 2010 24087.95 13.255
1 ABW Aruba 2015 27126.62 21.411
2 ABW Aruba 2020 21832.92 -7.521
12 ARE United Arab Emirates 2010 35064.26 31.330
13 ARE United Arab Emirates 2015 37380.57 30.540
inflation_rate total_investment unemployment_rate exports imports \
0 2.078 NaN 10.600 NaN NaN
1 0.475 NaN 7.298 NaN NaN
2 -1.338 NaN 13.997 NaN NaN
12 0.878 27.121 NaN 7.540 0.405
13 4.070 25.639 NaN 3.055 2.488
income_group
0 High income
1 High income
2 High income
12 High income
13 High income # Filter rows where total_investment is not NaN
non_null_investment = economies[economies['total_investment'].notna()]
non_null_investment.head() code country year gdp_percapita gross_savings inflation_rate \
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
5 AFG Afghanistan 2020 580.817 27.132 5.607
6 AGO Angola 2010 3641.440 34.833 14.480
7 AGO Angola 2015 4354.920 28.491 9.159
total_investment unemployment_rate exports imports income_group
3 30.269 NaN 9.768 32.285 Low income
4 18.427 NaN -11.585 15.309 Low income
5 16.420 NaN -10.424 2.892 Low income
6 28.197 NaN -3.266 -21.656 Lower middle income
7 34.202 NaN 6.721 -19.515 Lower middle income Conditions on multiple columns
# Filter rows where inflation_rate is less than 0 and income_group is 'Low income'
deflation_low_income = economies[(economies['inflation_rate'] < 0) & (economies['income_group'] == 'Low income')]
deflation_low_income.head() code country year gdp_percapita gross_savings inflation_rate \
4 AFG Afghanistan 2015 711.337 22.223 -0.662
42 BFA Burkina Faso 2010 648.365 20.194 -0.608
486 TCD Chad 2010 895.354 25.871 -2.110
total_investment unemployment_rate exports imports income_group
4 18.427 NaN -11.585 15.309 Low income
42 21.990 NaN 54.547 13.986 Low income
486 34.388 NaN -5.488 17.218 Low income # Filter rows where gdp_percapita is greater than 40,000 and year is less than or equal to 2016
top_gdp_2010_2015 = economies[(economies['gdp_percapita'] > 40000) & (economies['year'] <= 2015)]
top_gdp_2010_2015.head() code country year gdp_percapita gross_savings inflation_rate \
24 AUS Australia 2010 56459.80 23.105 2.863
25 AUS Australia 2015 51484.05 21.608 1.485
27 AUT Austria 2010 46955.17 25.463 1.693
28 AUT Austria 2015 44267.81 25.531 0.808
36 BEL Belgium 2010 44448.17 24.751 2.334
total_investment unemployment_rate exports imports income_group
24 26.369 5.208 5.717 15.507 High income
25 25.880 6.050 6.533 1.962 High income
27 22.608 4.817 13.131 11.970 High income
28 23.806 5.742 3.049 3.630 High income
36 23.127 8.308 8.484 7.171 High income Exercise #3: Cleaning and Preparing Data with Pandas
By completing this exercise, you will be able to use pandas to
- Handle missing data
- Convert a column to a different data type
- Rename a column
- Change a DataFrame’s index
- Filter a DataFrame
Handle Missing Data
Remove rows
# Remove rows with any missing values
populations_cleaned_any = populations.dropna(how='any')
populations_cleaned_any country_code country year fertility_rate life_expectancy \
0 ABW Aruba 2010 1.941 75.404
1 ABW Aruba 2015 1.972 75.683
2 ABW Aruba 2020 1.325 75.723
3 AFG Afghanistan 2010 6.099 60.851
4 AFG Afghanistan 2015 5.405 62.659
.. ... ... ... ... ...
640 ZMB Zambia 2015 4.793 61.208
641 ZMB Zambia 2020 4.379 62.380
642 ZWE Zimbabwe 2010 4.025 50.652
643 ZWE Zimbabwe 2015 3.849 59.591
644 ZWE Zimbabwe 2020 3.545 61.124
size official_state_name sovereignty \
0 100341 Aruba Netherlands
1 104257 Aruba Netherlands
2 106585 Aruba Netherlands
3 28189672 The Islamic Republic of Afghanistan UN member
4 33753499 The Islamic Republic of Afghanistan UN member
.. ... ... ...
640 16248230 The Republic of Zambia UN member
641 18927715 The Republic of Zambia UN member
642 12839771 The Republic of Zimbabwe UN member
643 14154937 The Republic of Zimbabwe UN member
644 15669666 The Republic of Zimbabwe UN member
continent region
0 North America Caribbean
1 North America Caribbean
2 North America Caribbean
3 Asia Southern and Central Asia
4 Asia Southern and Central Asia
.. ... ...
640 Africa Eastern Africa
641 Africa Eastern Africa
642 Africa Eastern Africa
643 Africa Eastern Africa
644 Africa Eastern Africa
[622 rows x 10 columns]# Remove rows only if all values are missing
populations_cleaned_all = populations.dropna(how='all')
populations_cleaned_all country_code country year fertility_rate life_expectancy \
0 ABW Aruba 2010 1.941 75.404
1 ABW Aruba 2015 1.972 75.683
2 ABW Aruba 2020 1.325 75.723
3 AFG Afghanistan 2010 6.099 60.851
4 AFG Afghanistan 2015 5.405 62.659
.. ... ... ... ... ...
640 ZMB Zambia 2015 4.793 61.208
641 ZMB Zambia 2020 4.379 62.380
642 ZWE Zimbabwe 2010 4.025 50.652
643 ZWE Zimbabwe 2015 3.849 59.591
644 ZWE Zimbabwe 2020 3.545 61.124
size official_state_name sovereignty \
0 100341 Aruba Netherlands
1 104257 Aruba Netherlands
2 106585 Aruba Netherlands
3 28189672 The Islamic Republic of Afghanistan UN member
4 33753499 The Islamic Republic of Afghanistan UN member
.. ... ... ...
640 16248230 The Republic of Zambia UN member
641 18927715 The Republic of Zambia UN member
642 12839771 The Republic of Zimbabwe UN member
643 14154937 The Republic of Zimbabwe UN member
644 15669666 The Republic of Zimbabwe UN member
continent region
0 North America Caribbean
1 North America Caribbean
2 North America Caribbean
3 Asia Southern and Central Asia
4 Asia Southern and Central Asia
.. ... ...
640 Africa Eastern Africa
641 Africa Eastern Africa
642 Africa Eastern Africa
643 Africa Eastern Africa
644 Africa Eastern Africa
[645 rows x 10 columns]# Remove rows with missing values in specific columns
populations_cleaned_subset = populations.dropna(subset=['fertility_rate', 'life_expectancy'])
populations_cleaned_subset country_code country year fertility_rate life_expectancy \
0 ABW Aruba 2010 1.941 75.404
1 ABW Aruba 2015 1.972 75.683
2 ABW Aruba 2020 1.325 75.723
3 AFG Afghanistan 2010 6.099 60.851
4 AFG Afghanistan 2015 5.405 62.659
.. ... ... ... ... ...
640 ZMB Zambia 2015 4.793 61.208
641 ZMB Zambia 2020 4.379 62.380
642 ZWE Zimbabwe 2010 4.025 50.652
643 ZWE Zimbabwe 2015 3.849 59.591
644 ZWE Zimbabwe 2020 3.545 61.124
size official_state_name sovereignty \
0 100341 Aruba Netherlands
1 104257 Aruba Netherlands
2 106585 Aruba Netherlands
3 28189672 The Islamic Republic of Afghanistan UN member
4 33753499 The Islamic Republic of Afghanistan UN member
.. ... ... ...
640 16248230 The Republic of Zambia UN member
641 18927715 The Republic of Zambia UN member
642 12839771 The Republic of Zimbabwe UN member
643 14154937 The Republic of Zimbabwe UN member
644 15669666 The Republic of Zimbabwe UN member
continent region
0 North America Caribbean
1 North America Caribbean
2 North America Caribbean
3 Asia Southern and Central Asia
4 Asia Southern and Central Asia
.. ... ...
640 Africa Eastern Africa
641 Africa Eastern Africa
642 Africa Eastern Africa
643 Africa Eastern Africa
644 Africa Eastern Africa
[622 rows x 10 columns]Remove columns
# Remove columns with any missing values
populations_no_missing_columns = populations.dropna(axis=1)
populations_no_missing_columns.head() country_code country year size \
0 ABW Aruba 2010 100341
1 ABW Aruba 2015 104257
2 ABW Aruba 2020 106585
3 AFG Afghanistan 2010 28189672
4 AFG Afghanistan 2015 33753499
official_state_name sovereignty continent \
0 Aruba Netherlands North America
1 Aruba Netherlands North America
2 Aruba Netherlands North America
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
region
0 Caribbean
1 Caribbean
2 Caribbean
3 Southern and Central Asia
4 Southern and Central Asia Replace missing values with specific value
# Replace missing values with a specific value (e.g., 0 for numerical columns,
# 'Unknown' for categorical columns)
populations_fill_value = populations.fillna({
'fertility_rate': 0,
'life_expectancy': 0,
'size': 0,
'continent': 'Unknown',
'region': 'Unknown'
})
populations_fill_value.head() country_code country year fertility_rate life_expectancy size \
0 ABW Aruba 2010 1.941 75.404 100341
1 ABW Aruba 2015 1.972 75.683 104257
2 ABW Aruba 2020 1.325 75.723 106585
3 AFG Afghanistan 2010 6.099 60.851 28189672
4 AFG Afghanistan 2015 5.405 62.659 33753499
official_state_name sovereignty continent \
0 Aruba Netherlands North America
1 Aruba Netherlands North America
2 Aruba Netherlands North America
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
region
0 Caribbean
1 Caribbean
2 Caribbean
3 Southern and Central Asia
4 Southern and Central Asia Convert a Column to a Different Data Type and Rename a Column
Convert a Column to a Different Data Type
# Convert the 'year' column to string type
populations['year'] = populations['year'].astype(str)
populations.dtypescountry_code object
country object
year object
fertility_rate float64
life_expectancy float64
size int64
official_state_name object
sovereignty object
continent object
region object
dtype: object# Convert it back to integer
populations['year'] = populations['year'].astype(int)
populations.dtypescountry_code object
country object
year int64
fertility_rate float64
life_expectancy float64
size int64
official_state_name object
sovereignty object
continent object
region object
dtype: objectRename a Column
# Rename the 'fertility_rate' column to 'fertility'
populations_renamed = populations.rename(columns={'fertility_rate': 'fertility'})
populations_renamed.head() country_code country year fertility life_expectancy size \
0 ABW Aruba 2010 1.941 75.404 100341
1 ABW Aruba 2015 1.972 75.683 104257
2 ABW Aruba 2020 1.325 75.723 106585
3 AFG Afghanistan 2010 6.099 60.851 28189672
4 AFG Afghanistan 2015 5.405 62.659 33753499
official_state_name sovereignty continent \
0 Aruba Netherlands North America
1 Aruba Netherlands North America
2 Aruba Netherlands North America
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
region
0 Caribbean
1 Caribbean
2 Caribbean
3 Southern and Central Asia
4 Southern and Central Asia Change a DataFrame’s Index and Filter a DataFrame
Change a DataFrame’s Index
# Set the 'country_code' column as the index
populations_indexed = populations.set_index('country_code')
populations_indexed.head() country year fertility_rate life_expectancy size \
country_code
ABW Aruba 2010 1.941 75.404 100341
ABW Aruba 2015 1.972 75.683 104257
ABW Aruba 2020 1.325 75.723 106585
AFG Afghanistan 2010 6.099 60.851 28189672
AFG Afghanistan 2015 5.405 62.659 33753499
official_state_name sovereignty continent \
country_code
ABW Aruba Netherlands North America
ABW Aruba Netherlands North America
ABW Aruba Netherlands North America
AFG The Islamic Republic of Afghanistan UN member Asia
AFG The Islamic Republic of Afghanistan UN member Asia
region
country_code
ABW Caribbean
ABW Caribbean
ABW Caribbean
AFG Southern and Central Asia
AFG Southern and Central Asia Filter a DataFrame
# Filter the DataFrame to include only rows where the 'continent' is 'Asia'
populations_asia = populations[populations['continent'] == 'Asia']
populations_asia.head() country_code country year fertility_rate life_expectancy \
3 AFG Afghanistan 2010 6.099 60.851
4 AFG Afghanistan 2015 5.405 62.659
5 AFG Afghanistan 2020 4.750 62.575
15 ARE United Arab Emirates 2010 1.790 78.334
16 ARE United Arab Emirates 2015 1.486 79.223
size official_state_name sovereignty continent \
3 28189672 The Islamic Republic of Afghanistan UN member Asia
4 33753499 The Islamic Republic of Afghanistan UN member Asia
5 38972230 The Islamic Republic of Afghanistan UN member Asia
15 8481771 The United Arab Emirates UN member Asia
16 8916899 The United Arab Emirates UN member Asia
region
3 Southern and Central Asia
4 Southern and Central Asia
5 Southern and Central Asia
15 Middle East
16 Middle East # Filter the DataFrame to include only rows where the 'year' is 2020
populations_2020 = populations[populations['year'] == 2020]
populations_2020.head() country_code country year fertility_rate life_expectancy size \
2 ABW Aruba 2020 1.325 75.723 106585
5 AFG Afghanistan 2020 4.750 62.575 38972230
8 AGO Angola 2020 5.371 62.261 33428486
11 ALB Albania 2020 1.400 76.989 2837849
14 AND Andorra 2020 NaN NaN 77700
official_state_name sovereignty continent \
2 Aruba Netherlands North America
5 The Islamic Republic of Afghanistan UN member Asia
8 The Republic of Angola UN member Africa
11 The Republic of Albania UN member Europe
14 The Principality of Andorra UN member Europe
region
2 Caribbean
5 Southern and Central Asia
8 Central Africa
11 Southern Europe
14 Southern Europe # Filter the DataFrame to include only rows where the 'fertility_rate' is greater than 2
populations_high_fertility = populations[populations['fertility_rate'] > 2]
populations_high_fertility.head() country_code country year fertility_rate life_expectancy size \
3 AFG Afghanistan 2010 6.099 60.851 28189672
4 AFG Afghanistan 2015 5.405 62.659 33753499
5 AFG Afghanistan 2020 4.750 62.575 38972230
6 AGO Angola 2010 6.194 56.726 23364185
7 AGO Angola 2015 5.774 60.655 28127721
official_state_name sovereignty continent \
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
5 The Islamic Republic of Afghanistan UN member Asia
6 The Republic of Angola UN member Africa
7 The Republic of Angola UN member Africa
region
3 Southern and Central Asia
4 Southern and Central Asia
5 Southern and Central Asia
6 Central Africa
7 Central Africa Walkthrough #4: Transforming and Aggregating Data with Pandas
Grouping data
grouped_data = economies.groupby('income_group')['gdp_percapita'].mean()
grouped_dataincome_group
High income 33781.737556
Low income 688.904493
Lower middle income 2329.609629
Not classified 7805.646667
Upper middle income 6679.059320
Name: gdp_percapita, dtype: float64Applying Functions
Applying a function element-wise with map()
# Convert income_group to uppercase using map()
economies_plus = economies.copy()
economies_plus['income_group_upper'] = economies['income_group'].map(str.upper)
economies_plus.head() code country year gdp_percapita gross_savings inflation_rate \
0 ABW Aruba 2010 24087.950 13.255 2.078
1 ABW Aruba 2015 27126.620 21.411 0.475
2 ABW Aruba 2020 21832.920 -7.521 -1.338
3 AFG Afghanistan 2010 631.490 59.699 2.179
4 AFG Afghanistan 2015 711.337 22.223 -0.662
total_investment unemployment_rate exports imports income_group \
0 NaN 10.600 NaN NaN High income
1 NaN 7.298 NaN NaN High income
2 NaN 13.997 NaN NaN High income
3 30.269 NaN 9.768 32.285 Low income
4 18.427 NaN -11.585 15.309 Low income
income_group_upper
0 HIGH INCOME
1 HIGH INCOME
2 HIGH INCOME
3 LOW INCOME
4 LOW INCOME Applying a Function to Groups with groupby() and agg()
# Calculate the median gdp_percapita and inflation_rate for each income_group
median_values = economies.groupby('income_group').agg({
'gdp_percapita': 'median',
'inflation_rate': 'median'
})
median_values gdp_percapita inflation_rate
income_group
High income 29529.305 0.8595
Low income 631.490 5.0490
Lower middle income 2012.150 4.4370
Not classified 10568.100 121.7380
Upper middle income 6083.870 2.7645Summary tables
# Create a pivot table of gdp_percapita and inflation_rate by income_group and year
pivot_table = pd.pivot_table(
economies,
values=['gdp_percapita', 'inflation_rate'],
index=['income_group'],
columns=['year'],
aggfunc='mean'
)
pivot_table gdp_percapita inflation_rate \
year 2010 2015 2020 2010
income_group
High income 33265.256167 33484.692333 34595.264167 2.168550
Low income 736.990261 685.146565 644.576652 5.915000
Lower middle income 2151.058283 2399.781453 2437.989151 5.778264
Not classified 11158.180000 10568.100000 1690.660000 28.187000
Upper middle income 6463.234694 6919.517551 6654.425714 4.251592
year 2015 2020
income_group
High income 0.910950 0.666333
Low income 7.187591 14.530182
Lower middle income 4.951170 18.002566
Not classified 121.738000 2355.150000
Upper middle income 3.186125 3.886408 Analyzing categorical data
Using cross-tabulation
# Show counts of income_group by year
cross_tab = pd.crosstab(economies['income_group'], economies['year'])
cross_tabyear 2010 2015 2020
income_group
High income 60 60 60
Low income 24 24 24
Lower middle income 53 53 53
Not classified 1 1 1
Upper middle income 49 49 49By getting group counts
# Count the occurrences of each income_group
income_group_counts = economies['income_group'].value_counts()
income_group_countsincome_group
High income 180
Lower middle income 159
Upper middle income 147
Low income 72
Not classified 3
Name: count, dtype: int64Exercise #4: Transforming and Aggregating Data with Pandas
By completing this exercise, you will be able to use pandas to
- Aggregate data effectively by grouping it
- Transform data by applying functions element-wise or to groups
- Create summary tables
- Analyze categorical data using cross-tabulation and counts
Grouping Data
# Group data by continent and calculate the mean life expectancy
grouped_data = populations.groupby('continent')['life_expectancy'].mean()
grouped_datacontinent
Africa 61.897980
Asia 73.611049
Europe 78.443978
North America 74.679029
Oceania 71.408114
South America 73.433389
Name: life_expectancy, dtype: float64Applying Functions
Applying a function element-wise with map()
# Convert continent to uppercase using map()
populations_plus = populations.copy()
populations_plus['continent_upper'] = populations['continent'].map(str.upper)
populations_plus.head() country_code country year fertility_rate life_expectancy size \
0 ABW Aruba 2010 1.941 75.404 100341
1 ABW Aruba 2015 1.972 75.683 104257
2 ABW Aruba 2020 1.325 75.723 106585
3 AFG Afghanistan 2010 6.099 60.851 28189672
4 AFG Afghanistan 2015 5.405 62.659 33753499
official_state_name sovereignty continent \
0 Aruba Netherlands North America
1 Aruba Netherlands North America
2 Aruba Netherlands North America
3 The Islamic Republic of Afghanistan UN member Asia
4 The Islamic Republic of Afghanistan UN member Asia
region continent_upper
0 Caribbean NORTH AMERICA
1 Caribbean NORTH AMERICA
2 Caribbean NORTH AMERICA
3 Southern and Central Asia ASIA
4 Southern and Central Asia ASIA Applying a function to groups with groupby() and agg()
# Calculate the median fertility rate and life expectancy for each continent
median_values = populations.groupby('continent').agg({
'fertility_rate': 'median',
'life_expectancy': 'median'
})
median_values fertility_rate life_expectancy
continent
Africa 4.5370 61.123500
Asia 2.1940 73.285500
Europe 1.5550 80.182927
North America 1.8350 74.821000
Oceania 3.2025 70.311000
South America 2.3195 73.688000Summary Tables
# Create a pivot table of fertility rate and life expectancy by continent and year
pivot_table = pd.pivot_table(
populations,
values=['fertility_rate', 'life_expectancy'],
index=['continent'],
columns=['year'],
aggfunc='mean'
)
pivot_table fertility_rate life_expectancy \
year 2010 2015 2020 2010 2015
continent
Africa 4.713426 4.424685 4.104963 59.746813 62.374598
Asia 2.559520 2.447560 2.245120 72.711414 73.914594
Europe 1.635364 1.614674 1.534233 77.781258 78.743396
North America 2.101258 1.960485 1.767803 74.193234 75.124420
Oceania 3.328375 3.059412 2.755941 70.761218 71.353058
South America 2.405833 2.266500 2.062083 73.001000 74.125333
year 2020
continent
Africa 63.572531
Asia 74.207140
Europe 78.807279
North America 74.720697
Oceania 72.110066
South America 73.173833 Analyzing Categorical Data
Using Cross-Tabulation
# Create a cross-tabulation of continent and year
cross_tab = pd.crosstab(populations['continent'], populations['year'])
cross_tabyear 2010 2015 2020
continent
Africa 54 54 54
Asia 50 50 50
Europe 46 46 46
North America 34 34 34
Oceania 19 19 19
South America 12 12 12By Getting Group Counts
# Count the occurrences of each region
region_counts = populations['region'].value_counts()
region_countsregion
Caribbean 66
Middle East 54
Eastern Africa 54
Western Africa 48
Southern Europe 45
Southern and Central Asia 42
South America 36
Southeast Asia 33
Eastern Europe 30
Western Europe 27
Central Africa 27
Central America 24
Micronesia 21
Eastern Asia 21
Northern Africa 18
Nordic Countries 18
Polynesia 15
Southern Africa 15
Melanesia 15
North America 12
Baltic Countries 9
British Islands 9
Australia and New Zealand 6
Name: count, dtype: int64Module 2: Data Visualization Basics with Matplotlib and Seaborn
Walkthrough #5: Creating Basic Plots with Matplotlib
Line plot
# Filter data for a specific country
afg_data = economies[economies['code'] == 'AFG']
# Line plot of gdp_percapita over the years
plt.figure(figsize=(10, 6))
plt.plot(afg_data['year'], afg_data['gdp_percapita'],
marker='o', linestyle='-', color='b')
plt.show();
Bar chart
# Filter data for Caribbean countries and the year 2020
caribbean_countries = ['ABW', 'BHS', 'BRB', 'DOM']
data_2020_caribbean = economies[(economies['year'] == 2020) & (economies['code'].isin(caribbean_countries))]
# Bar chart of gdp_percapita for different Caribbean countries in 2020
plt.figure(figsize=(10, 6))
plt.bar(x=data_2020_caribbean['code'],
height=data_2020_caribbean['gdp_percapita'],
color='g')
plt.xticks(rotation=45);
plt.show();
# Horizontal version
plt.figure(figsize=(10, 6))
plt.barh(y=data_2020_caribbean['code'],
width=data_2020_caribbean['gdp_percapita'],
color='g')
plt.show();
Adding labels and titles
# Filter data for a specific country
liberia_data = economies[economies['code'] == 'LBR']
# Line plot of gdp_percapita over the years with labels and titles
plt.figure(figsize=(10, 6))
plt.plot(liberia_data['year'], liberia_data['gdp_percapita'], marker='o', linestyle='-', color='r');
plt.xlabel('Year');
plt.ylabel('GDP Per Capita');
plt.title('GDP Per Capita Over Years for Liberia (LBR)');
plt.grid(True);
plt.show();
Adjusting axes and tick marks
# Bar chart of gdp_percapita for different Caribbean countries in 2020 with
# adjusted axes and tick marks
plt.figure(figsize=(10, 6))
plt.bar(data_2020_caribbean['code'], data_2020_caribbean['gdp_percapita'], color='purple')
plt.xlabel('Country Code')
plt.ylabel('GDP Per Capita')
plt.title('GDP Per Capita for Different Countries in 2020')
# Adjust axes
plt.ylim(0, max(data_2020_caribbean['gdp_percapita']) + 5000);
# Adjust tick marks
plt.xticks(rotation=45)([0, 1, 2, 3], [Text(0, 0, 'ABW'), Text(1, 0, 'BHS'), Text(2, 0, 'BRB'), Text(3, 0, 'DOM')])plt.yticks(range(0, int(max(data_2020_caribbean['gdp_percapita']) + 5000), 5000));
plt.grid(axis='y')
plt.show();
Exercise #5: Creating Basic Plots with Matplotlib
By completing this exercise, you will be able to use matplotlib to
- Create line plots and bar charts
- Add labels and titles
- Adjust axes and tick marks
Line Plot
import matplotlib.pyplot as plt
# Filter data for India
india_data = populations[populations['country_code'] == 'IND']
# Line plot of fertility rate over the years
plt.figure(figsize=(10, 6))
plt.plot(india_data['year'], india_data['fertility_rate'], marker='o', linestyle='-', color='b')
plt.show();
Bar Chart
# Filter data for selected Asian countries and the year 2020
asian_countries = ['CHN', 'IND', 'IDN', 'PAK', 'BGD']
data_2020_asia = populations[(populations['year'] == 2020) & (populations['country_code'].isin(asian_countries))]
# Bar chart of population size for selected Asian countries in 2020
plt.figure(figsize=(10, 6))
plt.bar(data_2020_asia['country_code'], data_2020_asia['size'], color='g')
plt.show();
Adding Labels and Titles
# Filter data for Nigeria
nigeria_data = populations[populations['country_code'] == 'NGA']
# Line plot of life expectancy over the years with labels and titles
plt.figure(figsize=(10, 6))
plt.plot(nigeria_data['year'], nigeria_data['life_expectancy'],
marker='o', linestyle='-', color='r')
plt.xlabel('Year')
plt.ylabel('Life Expectancy')
plt.title('Life Expectancy Over Years for Nigeria (NGA)')
plt.grid(True)
plt.show();
Adjusting Axes and Tick Marks
# Filter data for selected African countries ('NGA', 'ETH', 'EGY', 'ZAF', 'DZA')
# and the year 2020
african_countries = ['NGA', 'ETH', 'EGY', 'ZAF', 'DZA']
# Need to convert year back to an integer?
populations['year'] = populations['year'].astype(int)
data_2020_africa = populations[(populations['year'] == 2020) & (populations['country_code'].isin(african_countries))]
# Bar chart of fertility rate for selected African countries in 2020 with
# adjusted axes and tick marks
plt.figure(figsize=(10, 6))
plt.bar(data_2020_africa['country_code'], data_2020_africa['fertility_rate'], color='purple')
plt.xlabel('Country Code')
plt.ylabel('Fertility Rate')
plt.title('Fertility Rate for Selected African Countries in 2020')
# Adjust axes
plt.ylim(0, max(data_2020_africa['fertility_rate']) + 1)(0.0, 6.309)# Adjust tick marks
plt.xticks(rotation=45)([0, 1, 2, 3, 4], [Text(0, 0, 'DZA'), Text(1, 0, 'EGY'), Text(2, 0, 'ETH'), Text(3, 0, 'NGA'), Text(4, 0, 'ZAF')])plt.yticks(range(0, int(max(data_2020_africa['fertility_rate']) + 1), 1))([<matplotlib.axis.YTick object at 0x120a52490>, <matplotlib.axis.YTick object at 0x120bebc50>, <matplotlib.axis.YTick object at 0x120be8550>, <matplotlib.axis.YTick object at 0x120be8cd0>, <matplotlib.axis.YTick object at 0x120be9450>, <matplotlib.axis.YTick object at 0x120be9bd0>], [Text(0, 0, '0'), Text(0, 1, '1'), Text(0, 2, '2'), Text(0, 3, '3'), Text(0, 4, '4'), Text(0, 5, '5')])plt.grid(axis='y')
plt.show();
Walkthrough #6: Data Visualization Techniques with Seaborn
Heatmap
# Select only the numeric columns
numeric_cols = economies.select_dtypes(include=['float64', 'int64']).columns
numeric_economies = economies[numeric_cols]
# Calculate correlation matrix
corr_matrix = numeric_economies.corr()
# Create heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap')
plt.show();
Pair plot
sns.pairplot(economies, vars=['gdp_percapita', 'gross_savings', 'inflation_rate', 'total_investment'])
plt.suptitle('Pair Plot of Numerical Columns', y=1)
plt.show();
Violin plot
plt.figure(figsize=(10, 6))
sns.violinplot(x='income_group', y='gdp_percapita', data=economies)
plt.xlabel('Income Group')
plt.ylabel('GDP Per Capita')
plt.title('Violin Plot of GDP Per Capita by Income Group')
plt.show();
Customizing Seaborn plots
# Bar plot with customization
plt.figure(figsize=(10, 6))
sns.barplot(x='code', y='gdp_percapita', hue='code', data=data_2020_caribbean, palette='viridis')
plt.xlabel('Country Code')
plt.ylabel('GDP Per Capita')
plt.title('GDP Per Capita for Different Caribbean Countries in 2020')
# Customizing axes and tick marks
plt.ylim(0, max(data_2020_caribbean['gdp_percapita']) + 5000);
plt.xticks(rotation=60);
plt.yticks(range(0, int(max(data_2020_caribbean['gdp_percapita']) + 5000), 5000));
plt.grid(axis='y')
plt.show();
Exercise #6: Data Visualization Techniques with Seaborn
By completing this exercise, you will be able to use seaborn to
- Create heatmaps
- Design pair plots and violin plots
- Customize Seaborn plots
Heatmap
import seaborn as sns
import matplotlib.pyplot as plt
# Select only the numeric columns
numeric_cols = populations.select_dtypes(include=['float64', 'int64']).columns
numeric_populations = populations[numeric_cols]
# Calculate correlation matrix
pop_corr_matrix = numeric_populations.corr()
# Create heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(pop_corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap')
plt.show();
Pair Plot
import seaborn as sns
import matplotlib.pyplot as plt
# Pair plot of fertility rate, life expectancy, and population size
sns.pairplot(populations, vars=['fertility_rate', 'life_expectancy', 'size'])
plt.suptitle('Pair Plot of Selected Numerical Columns', y=1)
plt.show();
Violin Plot
import seaborn as sns
import matplotlib.pyplot as plt
# Violin plot of fertility rate by continent
plt.figure(figsize=(10, 6))
sns.violinplot(x='continent', y='fertility_rate', data=populations)
plt.xlabel('Continent')
plt.ylabel('Fertility Rate')
plt.title('Violin Plot of Fertility Rate by Continent')
plt.show();
Customizing Seaborn Plots
import seaborn as sns
import matplotlib.pyplot as plt
# Filter data for selected European countries ('DEU', 'FRA', 'ITA', 'ESP', 'GBR')
# and the year 2020
european_countries = ['DEU', 'FRA', 'ITA', 'ESP', 'GBR']
data_2020_europe = populations[(populations['year'] == 2020) & (populations['country_code'].isin(european_countries))]
# Bar plot with customization
plt.figure(figsize=(10, 6))
sns.barplot(x='country_code', y='life_expectancy', hue='country_code', data=data_2020_europe, palette='viridis')
plt.xlabel('Country Code')
plt.ylabel('Life Expectancy')
plt.title('Life Expectancy for Selected European Countries in 2020')
# Customizing axes and tick marks
plt.ylim(0, max(data_2020_europe['life_expectancy']) + 10);
plt.xticks(rotation=45);
plt.yticks(range(0, int(max(data_2020_europe['life_expectancy']) + 10), 10));
plt.grid(axis='y')
plt.show();
Module 3: Interactive Data Visualization with Plotly
Walkthrough #7: Interactive Charts and Dashboards with Plotly
Basic interactive chart
# Filter data for a specific country
afg_data = economies[economies['code'] == 'AFG']
# Create an interactive line chart
fig = px.line(afg_data, x='year', y='gdp_percapita', title='GDP Per Capita Over Years for Afghanistan (AFG)')
fig.show();Adding interactive elements
# Create an interactive scatter plot
fig = px.scatter(economies, x='gdp_percapita', y='gross_savings', color='income_group',
hover_name='code', title='GDP Per Capita vs. Gross Savings',
labels={'gdp_percapita': 'GDP Per Capita', 'gross_savings': 'Gross Savings (%)'})
# Add hover, zoom, and selection tools
fig.update_traces(marker=dict(size=10), selector=dict(mode='markers'))fig.update_layout(hovermode='closest')
fig.show();Designing a simple dashboard
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Filter data for the year 2020
data_2020 = economies[economies['year'] == 2020]
# Create a subplot figure with 1 row and 2 columns
fig = make_subplots(rows=1, cols=2,
subplot_titles=('GDP Per Capita Over Years for Afghanistan',
'GDP Per Capita for Different Countries in 2020'))
# Line chart of GDP Per Capita for Afghanistan
afg_data = economies[economies['code'] == 'AFG']
line_chart = go.Scatter(x=afg_data['year'], y=afg_data['gdp_percapita'], mode='lines+markers', name='Afghanistan')
fig.add_trace(line_chart, row=1, col=1)# Bar chart of GDP Per Capita for different countries in 2020
bar_chart = go.Bar(x=data_2020['code'], y=data_2020['gdp_percapita'], name='2020')
fig.add_trace(bar_chart, row=1, col=2)# Update layout
fig.update_layout(title_text='Simple Dashboard with Multiple Charts', showlegend=False)fig.show();Exercise #7: Interactive Charts and Dashboards with Plotly
By completing this exercise, you will be able to use plotly to
- Create a basic interactive chart
- Add interactive elements: hover, zoom, and selection tools
- Design a simple dashboard with multiple charts
Basic Interactive Chart
import plotly.express as px
# Filter data for a specific country (Brazil)
bra_data = populations[populations['country_code'] == 'BRA']
# Create an interactive line chart (Fertility Rate Over Years)
fig = px.line(bra_data, x='year', y='fertility_rate', title='Fertility Rate Over Years for Brazil (BRA)')
fig.show();Adding Interactive Elements
import plotly.express as px
# Create an interactive scatter plot
fig = px.scatter(populations, x='fertility_rate', y='life_expectancy', color='continent',
hover_name='country', title='Fertility Rate vs. Life Expectancy',
labels={'fertility_rate': 'Fertility Rate', 'life_expectancy': 'Life Expectancy'})
# Add hover, zoom, and selection tools
fig.update_traces(marker=dict(size=10), selector=dict(mode='markers'))fig.update_layout(hovermode='closest')
fig.show();Designing a Simple Dashboard
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Filter data for the year 2020
data_2020 = populations[populations['year'] == 2020]
# Create a subplot figure with 1 row and 2 columns
fig = make_subplots(rows=1, cols=2, subplot_titles=('Life Expectancy Over Years for Brazil', 'Life Expectancy for Different Countries in 2020'))
# Line chart of Life Expectancy for Brazil
bra_data = populations[populations['country_code'] == 'BRA']
line_chart = go.Scatter(x=bra_data['year'], y=bra_data['life_expectancy'], mode='lines+markers', name='Brazil')
fig.add_trace(line_chart, row=1, col=1)# Bar chart of Life Expectancy for South American countries in 2020
south_american_data_2020 = data_2020[data_2020['continent'] == 'South America']
bar_chart = go.Bar(x=south_american_data_2020['country'], y=south_american_data_2020['life_expectancy'], name='2020')
fig.add_trace(bar_chart, row=1, col=2)# Update layout to add a title and hide the legend
fig.update_layout(title_text='Simple Dashboard with Multiple Charts', showlegend=False)fig.show();Walkthrough #8: Creating a Dynamic Data Report
Selecting relevant data
# Select relevant data for the year 2020 and specific columns
selected_data = economies[economies['year'] == 2020][['code', 'gdp_percapita', 'gross_savings', 'inflation_rate', 'income_group']]
selected_data.head() code gdp_percapita gross_savings inflation_rate income_group
2 ABW 21832.920 -7.521 -1.338 High income
5 AFG 580.817 27.132 5.607 Low income
8 AGO 2012.150 22.399 22.277 Lower middle income
11 ALB 5286.680 13.255 1.603 Upper middle income
14 ARE 31982.230 28.223 -2.074 High incomeBuilding a dynamic report
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Create a subplot figure with 3 rows
fig = make_subplots(rows=3, cols=1,
subplot_titles=('GDP Per Capita vs. Gross Savings',
'GDP Per Capita by Country and Income Group',
'Gross Savings by Country and Income Group'))
# Add scatter plot
fig.add_trace(go.Scatter(x=selected_data['gdp_percapita'], y=selected_data['gross_savings'],
mode='markers',
marker=dict(color=selected_data['income_group'].astype('category').cat.codes),
text=selected_data['code'], name='Scatter'),
row=1, col=1)# Add bar chart
fig.add_trace(go.Bar(x=selected_data['code'], y=selected_data['gdp_percapita'],
marker=dict(color=selected_data['income_group'].astype('category').cat.codes), name='Bar'),
row=2, col=1)# Add another scatter plot
fig.add_trace(go.Scatter(x=selected_data['code'], y=selected_data['gross_savings'],
mode='markers',
marker=dict(color=selected_data['income_group'].astype('category').cat.codes), text=selected_data['code'], name='Scatter'),
row=3, col=1)# Update layout
fig.update_layout(title_text='Dynamic Data Report for Economic Indicators (2020)', showlegend=False, height=900)
fig.show();Adding contextual text and summaries
import plotly.io as pio
import plotly.graph_objects as go
# Create a subplot figure with 3 rows
fig = make_subplots(rows=3, cols=1,
subplot_titles=('GDP Per Capita vs. Gross Savings',
'GDP Per Capita by Country and Income Group',
'Gross Savings by Country and Income Group'))
# Add scatter plot
fig.add_trace(go.Scatter(x=selected_data['gdp_percapita'], y=selected_data['gross_savings'],
mode='markers',
marker=dict(color=selected_data['income_group'].astype('category').cat.codes),
text=selected_data['code'], name='Scatter'),
row=1, col=1)# Add bar chart
fig.add_trace(go.Bar(x=selected_data['code'], y=selected_data['gdp_percapita'],
marker=dict(color=selected_data['income_group'].astype('category').cat.codes), name='Bar'),
row=2, col=1)# Add another scatter plot
fig.add_trace(go.Scatter(x=selected_data['code'], y=selected_data['gross_savings'],
mode='markers',
marker=dict(color=selected_data['income_group'].astype('category').cat.codes),
text=selected_data['code'], name='Scatter'),
row=3, col=1)# Update layout
fig.update_layout(
title_text='Dynamic Data Report for Economic Indicators (2020)',
showlegend=False,
height=900,
annotations=[
go.layout.Annotation(
text='''This report presents key economic indicators for various countries in 2020, categorized by income group. ''',
xref='paper', yref='paper', x=0.5, y=1, showarrow=False, font=dict(size=14)
)
]
)# Add summaries below each subplot
fig.add_annotation(text='The scatter plot reveals a positive correlation between GDP per Capita and Gross Savings, especially for high-income countries.', xref='paper', yref='paper', x=0, y=0.75, showarrow=False, font=dict(size=12))fig.add_annotation(text='The bar chart shows that high-income countries generally have higher GDP per Capita compared to low-income countries.', xref='paper', yref='paper', x=0, y=0.30, showarrow=False, font=dict(size=12))fig.add_annotation(text='The scatter plot indicates no clear relationship between income group and gross savings.', xref='paper', yref='paper', x=0, y=-0.1, showarrow=False, font=dict(size=12))
fig.show();Exercise #8: Creating a Dynamic Data Report
By completing this exercise, you will be able to use pandas and plotly to
- Select relevant data
- Build a dynamic report
- Add contextual text and summaries
Selecting Relevant Data
# Select relevant data for the year 2020 and specific columns (country_code, fertility_rate, life_expectancy, continent)
pop_selected_data = populations[populations['year'] == 2020][['country_code', 'fertility_rate', 'life_expectancy', 'continent']]
pop_selected_data.head() country_code fertility_rate life_expectancy continent
2 ABW 1.325 75.723 North America
5 AFG 4.750 62.575 Asia
8 AGO 5.371 62.261 Africa
11 ALB 1.400 76.989 Europe
14 AND NaN NaN EuropeBuilding a Dynamic Report
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Create a subplot figure with 3 rows and subplot titles
fig = make_subplots(rows=3, cols=1,
subplot_titles=('Fertility Rate vs. Life Expectancy',
'Fertility Rate by Country and Continent',
'Life Expectancy by Country and Continent'))
# Adding a scatter plot trace to the figure
# - x-axis: 'fertility_rate' from the selected population data
# - y-axis: 'life_expectancy' from the selected population data
# - mode: 'markers' to display points
# - marker color: based on the 'continent' category codes, to differentiate points by continent
# - text: 'country_code' to show country codes on hover
# - name: 'Scatter' to label this trace
# The trace is added to the first row and first column of the subplot grid
fig.add_trace(go.Scatter(x=pop_selected_data['fertility_rate'], y=pop_selected_data['life_expectancy'],
mode='markers',
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes),
text=pop_selected_data['country_code'], name='Scatter'),
row=1, col=1)# Adding a bar chart trace to the figure
# - x-axis: 'country_code' from the selected population data
# - y-axis: 'fertility_rate' from the selected population data
# - marker color: based on the 'continent' category codes, to differentiate bars by continent
# - name: 'Bar' to label this trace
# The trace is added to the second row and first column of the subplot grid
fig.add_trace(go.Bar(x=pop_selected_data['country_code'], y=pop_selected_data['fertility_rate'],
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes), name='Bar'),
row=2, col=1)# Adding a scatter plot trace to the figure
# - x-axis: 'country_code' from the selected population data
# - y-axis: 'life_expectancy' from the selected population data
# - mode: 'markers' to display points
# - marker color: based on the 'continent' category codes, to differentiate points by continent
# - name: 'Scatter' to label this trace
# The trace is added to the third row and first column of the subplot grid
fig.add_trace(go.Scatter(x=pop_selected_data['country_code'], y=pop_selected_data['life_expectancy'],
mode='markers',
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes), name='Scatter'),
row=3, col=1)# Update layout to include title, hide legend, and set height to 900
fig.update_layout(title_text='Dynamic Data Report for Population Indicators (2020)', showlegend=False, height=900)
fig.show();Adding Contextual Text and Summaries
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Create a subplot figure with 3 rows
fig = make_subplots(rows=3, cols=1,
subplot_titles=('Fertility Rate vs. Life Expectancy',
'Fertility Rate by Country and Continent',
'Life Expectancy by Country and Continent'))
# Add scatter plot
fig.add_trace(go.Scatter(x=pop_selected_data['fertility_rate'], y=pop_selected_data['life_expectancy'],
mode='markers',
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes),
text=pop_selected_data['country_code'], name='Scatter'),
row=1, col=1)# Add bar chart
fig.add_trace(go.Bar(x=pop_selected_data['country_code'], y=pop_selected_data['fertility_rate'],
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes), name='Bar'),
row=2, col=1)# Add another scatter plot
fig.add_trace(go.Scatter(x=pop_selected_data['country_code'], y=pop_selected_data['life_expectancy'],
mode='markers',
marker=dict(color=pop_selected_data['continent'].astype('category').cat.codes), name='Scatter'),
row=3, col=1)# Update layout
fig.update_layout(
title_text='Dynamic Data Report for Population Indicators (2020)',
showlegend=False,
height=900,
annotations=[
go.layout.Annotation(
text='''This report presents key population-based indicators for various countries in 2020, categorized by continent. ''',
xref='paper', yref='paper', x=0.5, y=1, showarrow=False, font=dict(size=14)
)
]
)# Add summaries below each subplot
fig.add_annotation(text='A negative correlation between Fertility Rate and Life Expectancy.', xref='paper', yref='paper', x=0, y=0.75, showarrow=False, font=dict(size=12))fig.add_annotation(text='Fertility rates vary significantly across countries, with African countries generally exhibiting higher fertility rates.', xref='paper', yref='paper', x=0, y=0.30, showarrow=False, font=dict(size=12))fig.add_annotation(text='Life expectancy varies across countries and continents, with European tending to have higher life expectancies.', xref='paper', yref='paper', x=0, y=-0.1, showarrow=False, font=dict(size=12))
fig.show();Module 4: Real-World Data Analysis Project
Walkthrough #9: Interactive Charts and Dashboards with Plotly
Selecting a Dataset
Questions to Ask:
- What industry problem or area of interest does the dataset align with?
- Is the dataset relevant to economic analysis, market research, policy planning, or another industry?
- Does the dataset provide sufficient complexity and scope for a thorough analysis?
- Does it include multiple variables and data points across different time periods and categories (e.g., income groups, countries)?
- What specific questions or hypotheses do we want to explore with this dataset?
- Are we interested in comparing economic indicators across countries, understanding the impact of GDP per capita on other variables, or identifying trends over time?
Example:
- Dataset: The
economiesdataset. - Industry Problem: Understanding economic disparities between countries and the impact of economic indicators on overall economic health.
- Specific Questions:
- How do GDP per capita and gross savings vary across different income groups?
- How has the inflation rate changed over time for specified income groups?
Applying Cleaning, Transforming, and Analysis Techniques
Questions to Ask:
- What cleaning steps are necessary to prepare the data for analysis?
- Are there any missing values that need to be handled? Are there any inconsistencies in data types?
- What transformations are required to make the data analysis-ready?
- Do we need to create new columns, filter specific rows, or aggregate data by certain categories?
- How can we analyze the data to uncover patterns, trends, or anomalies?
- What statistical methods or visualizations can we use to explore relationships between variables?
Example:
Cleaning:
# Handle missing values economies_cleaned = economies.fillna({ 'gdp_percapita': economies['gdp_percapita'].mean(), 'gross_savings': economies['gross_savings'].mean(), 'inflation_rate': economies['inflation_rate'].mean(), 'total_investment': economies['total_investment'].mean(), 'unemployment_rate': economies['unemployment_rate'].mean(), 'exports': economies['exports'].mean(), 'imports': economies['imports'].mean() }) # Convert categorical variables to category type economies_cleaned['income_group'] = economies_cleaned['income_group'].astype('category') economies_cleanedcode country year gdp_percapita gross_savings inflation_rate \ 0 ABW Aruba 2010 24087.950 13.255000 2.078 1 ABW Aruba 2015 27126.620 21.411000 0.475 2 ABW Aruba 2020 21832.920 -7.521000 -1.338 3 AFG Afghanistan 2010 631.490 59.699000 2.179 4 AFG Afghanistan 2015 711.337 22.223000 -0.662 .. ... ... ... ... ... ... 556 ZMB Zambia 2015 1310.460 40.103000 10.107 557 ZMB Zambia 2020 981.311 36.030000 16.350 558 ZWE Zimbabwe 2010 975.851 20.641665 3.045 559 ZWE Zimbabwe 2015 1425.010 20.641665 -2.410 560 ZWE Zimbabwe 2020 1385.040 20.641665 557.210 total_investment unemployment_rate exports imports \ 0 25.348976 10.600000 -0.844275 0.813121 1 25.348976 7.298000 -0.844275 0.813121 2 25.348976 13.997000 -0.844275 0.813121 3 30.269000 8.894619 9.768000 32.285000 4 18.427000 8.894619 -11.585000 15.309000 .. ... ... ... ... 556 42.791000 8.894619 -11.407000 0.696000 557 34.514000 8.894619 1.143000 2.635000 558 25.348976 8.894619 -0.844275 0.813121 559 25.348976 8.894619 -0.844275 0.813121 560 25.348976 8.894619 -0.844275 0.813121 income_group 0 High income 1 High income 2 High income 3 Low income 4 Low income .. ... 556 Lower middle income 557 Lower middle income 558 Lower middle income 559 Lower middle income 560 Lower middle income [561 rows x 11 columns]Transforming:
# Create new columns for analysis economies_cleaned['gdp_growth'] = economies_cleaned.groupby('code')['gdp_percapita'].pct_change() economies_cleanedcode country year gdp_percapita gross_savings inflation_rate \ 0 ABW Aruba 2010 24087.950 13.255000 2.078 1 ABW Aruba 2015 27126.620 21.411000 0.475 2 ABW Aruba 2020 21832.920 -7.521000 -1.338 3 AFG Afghanistan 2010 631.490 59.699000 2.179 4 AFG Afghanistan 2015 711.337 22.223000 -0.662 .. ... ... ... ... ... ... 556 ZMB Zambia 2015 1310.460 40.103000 10.107 557 ZMB Zambia 2020 981.311 36.030000 16.350 558 ZWE Zimbabwe 2010 975.851 20.641665 3.045 559 ZWE Zimbabwe 2015 1425.010 20.641665 -2.410 560 ZWE Zimbabwe 2020 1385.040 20.641665 557.210 total_investment unemployment_rate exports imports \ 0 25.348976 10.600000 -0.844275 0.813121 1 25.348976 7.298000 -0.844275 0.813121 2 25.348976 13.997000 -0.844275 0.813121 3 30.269000 8.894619 9.768000 32.285000 4 18.427000 8.894619 -11.585000 15.309000 .. ... ... ... ... 556 42.791000 8.894619 -11.407000 0.696000 557 34.514000 8.894619 1.143000 2.635000 558 25.348976 8.894619 -0.844275 0.813121 559 25.348976 8.894619 -0.844275 0.813121 560 25.348976 8.894619 -0.844275 0.813121 income_group gdp_growth 0 High income NaN 1 High income 0.126149 2 High income -0.195148 3 Low income NaN 4 Low income 0.126442 .. ... ... 556 Lower middle income -0.099990 557 Lower middle income -0.251171 558 Lower middle income NaN 559 Lower middle income 0.460274 560 Lower middle income -0.028049 [561 rows x 12 columns]Analyzing:
import seaborn as sns import matplotlib.pyplot as plt plt.clf() # Analyze the relationship between GDP per capita and gross savings sns.scatterplot(data=economies_cleaned, x='gdp_percapita', y='gross_savings', hue='income_group') plt.title('GDP Per Capita vs. Gross Savings by Income Group') plt.show()
plt.clf() # Analyze the trend of inflation rate over time for all classified income groups classified_data = economies_cleaned[economies_cleaned['income_group'] != 'Not classified'] sns.lineplot(data=classified_data, x='year', y='inflation_rate', hue='income_group', errorbar=None) plt.title('Inflation Rate Over Time by Specified Income Group') plt.legend(title='Income Group', labels=classified_data['income_group'].unique()) plt.show();
Initial Findings and Interpretation
Questions to Ask:
- What do the initial findings tell us about the data?
- Are there any notable patterns or trends in the data? Are there any unexpected results?
- How do these insights relate to the problem defined earlier?
- Do the findings help us understand economic disparities between countries? Do they provide insights into the impact of certain economic indicators?
- What hypotheses can we test based on the initial results?
- Can we test hypotheses about the relationship between GDP per capita and other economic indicators? Can we refine our analysis to explore these hypotheses further?
Example:
- Initial Findings:
- GDP per Capita vs. Gross Savings: The scatter plot shows that high-income countries generally have higher GDP per capita and gross savings. There seems to be a slight positive correlation between these two indicators.
- Inflation Rate Over Time: The line plot indicates that inflation rates vary significantly over time and across different income groups. Low and lower middle income countries tend to experience higher volatility in inflation rates.
- Interpretation:
- These findings suggest that economic health, as measured by GDP per capita and gross savings, is strongly influenced by the income group of a country. High-income countries appear to have more stable and higher economic performance.
- The volatility in inflation rates among low-income countries may indicate economic instability, which could be a key area for policy intervention.
- Hypotheses:
- Hypothesis 1: High-income countries have a higher average GDP per capita and gross savings compared to low-income countries.
- Hypothesis 2: Low-income countries experience greater volatility in inflation rates compared to high-income countries.
- Next Steps:
- Conduct further analysis to test these hypotheses, using statistical methods to confirm the observed patterns.
- Explore other economic indicators to gain a more comprehensive understanding of economic disparities and trends.
By following these steps, you can effectively select, clean, transform, and analyze the economies dataset to gain valuable insights and address common industry problems or research questions.
Walkthrough #10: Finalizing and Presenting Your Data Analysis Project
Integrate Feedback to Refine the Analysis
Questions to Ask:
- What feedback have you received from peers, stakeholders, or mentors?
- Is there feedback on the clarity of the analysis, choice of visualizations, or the comprehensiveness of the analysis?
- How can you incorporate this feedback into your analysis?
- Are there additional variables that need to be analyzed? Do you need to clean the data further or adjust the visualizations?
- What new questions or hypotheses have emerged from the feedback?
- Does the feedback suggest new directions for the analysis or areas that need more focus?
Example:
- Feedback:
- Peers suggested that the analysis should also consider the impact of unemployment rates.
- Stakeholders requested more clarity on the relationship between GDP per capita and inflation rates across different income groups.
- Refining the Analysis:
- Additional data needs to be found to meet the request for more clarity. Or maybe a further drilldown on specific countries would be helpful?
Finalize the Presentation with Impactful Visuals and Narrative
Questions to Ask:
- What are the key insights from the analysis that need to be highlighted?
- What are the most important findings that should be communicated to the audience?
- How can you create impactful visuals that clearly convey these insights?
- What types of charts or visualizations best represent the data and findings?
- What narrative will you use to guide the audience through the presentation?
- How will you structure the presentation to tell a compelling story with the data?
Example:
Key Insights:
- High-income countries have higher GDP per capita and gross savings.
- There is a positive correlation between GDP per capita and gross savings.
- Low-income countries experience greater volatility in inflation rates.
- Unemployment rates vary significantly across income groups.
Impactful Visuals:
import plotly.express as px # Bar chart of GDP per Capita by Country and Income Group bar_fig = px.bar(economies_cleaned, x='code', y='gdp_percapita', color='income_group', title='GDP Per Capita by Country and Income Group (2020)', labels={'gdp_percapita': 'GDP Per Capita', 'code': 'Country Code'}) bar_fig.show(); # Scatter plot of GDP per Capita vs. Gross Savings by Income Group scatter_fig = px.scatter(economies_cleaned, x='gdp_percapita', y='gross_savings', color='income_group', hover_name='code', title='GDP Per Capita vs. Gross Savings (2020)', labels={'gdp_percapita': 'GDP Per Capita', 'gross_savings': 'Gross Savings (%)'}) scatter_fig.show(); # Scatter plot of GDP per Capita vs. Unemployment Rate by Income Group scatter_fig_2 = px.scatter(economies_cleaned, x='gdp_percapita', y='unemployment_rate', color='income_group', hover_name='code', title='GDP Per Capita vs. Unemployment Rate (2020)', labels={'gdp_percapita': 'GDP Per Capita', 'unemployment_rate': 'Unemployment Rate (%)'}) scatter_fig_2.show();Narrative:
- Introduction: Introduce the dataset and the industry problem. Explain why understanding economic indicators across different income groups is important.
- Key Findings: Present the key findings using the visualizations created. Highlight the relationship between GDP per capita, gross savings, inflation rates, and unemployment rates.
- Detailed Analysis: Dive deeper into each key finding, providing more context and interpretation. Explain the significance of the trends and patterns observed in the data.
- Conclusion: Summarize the insights and discuss potential implications for policy or business decisions. Suggest areas for further research or analysis based on the findings.
Rehearse the Presentation
Questions to Ask:
- How will you structure your presentation to ensure a smooth flow?
- What order will you present the visualizations and insights? How will you transition between different sections?
- How will you engage your audience and ensure they understand the key points?
- What techniques will you use to highlight important information and keep the audience’s attention?
- What potential questions or feedback might you receive, and how will you address them?
- How will you prepare for questions about the data, analysis methods, or findings?
Example:
- Structuring the Presentation:
- Start with an overview of the dataset and the industry problem.
- Move on to the key findings, using the most impactful visualizations to illustrate each point.
- Provide a detailed analysis of each finding, explaining the significance and implications.
- Conclude with a summary of insights and suggestions for further research.
- Engaging the Audience:
- Use clear and concise language to explain complex concepts.
- Highlight key points using annotations or callouts on the visualizations.
- Encourage questions and interaction to keep the audience engaged.
- Preparing for Questions:
- Anticipate common questions about the data sources, cleaning methods, and analysis techniques.
- Prepare explanations for any limitations of the data or analysis.
- Be ready to discuss potential next steps and areas for further research based on the findings.
By following these steps, you can effectively integrate feedback, finalize your presentation with impactful visuals and narrative, and rehearse to ensure a smooth and engaging delivery.