Line chart 1 – data from a row

This template is made for you to customize.

Sometimes we want to work with specific rows in a data set, especially when the data set appears to be in a list format. This type of data set is common when we are working on with objects that we can easily compare with each other, such as information between different countries, CO\(^2\) emissions of different food supplies etc. In this template we use a data set that can be found from here, showing country-specific energy consumption per capita from 1960 to 2015.

Go ahead and use this template if you want to make a line chart from a row of your data file (or you can use the example data set). You just have to replace the data set and variable names, and add some text. If you have any problems and you cannot find an answer from Google, please do contact us! We are happy to help you.

# Import libraries 

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Read csv file, which is downloaded to GitHub.
# Head-command shows the data set, check that everything looks good.

energy_per_capita = pd.read_csv('https://raw.githubusercontent.com/opendata-education/Harjoittelu/main/data/energy_use_per_person.csv')
energy_per_capita.head()

	country	1960	1961	1962	1963	1964	1965	1966	1967	1968	...	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
0	Angola	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	459	472	492	515	521	522	552	534	545	NaN
1	Albania	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	707	680	711	732	729	765	688	801	808	NaN
2	United Arab Emirates	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	8720	8130	8370	7570	7220	7190	7480	7600	7650	NaN
3	Argentina	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	1850	1860	1940	1870	1930	1950	1940	1970	2030	NaN
4	Armenia	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	865	973	1030	904	863	944	1030	1000	1020	NaN

5 rows × 57 columns

# With this command we can check the row of the wanted country, so we can use that information later.

energy_per_capita.loc[energy_per_capita['country'] == 'Finland']

	country	1960	1961	1962	1963	1964	1965	1966	1967	1968	...	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
51	Finland	2200	2250	2360	2480	2680	2890	3070	3130	3340	...	7110	6970	6670	6270	6830	6540	6280	6120	6210	5920

1 rows × 57 columns

# Prepare the data for x-axis - collect the column headers into a list
# Use np.int_ or np.float_ commands. This changes the objects in the list to values
# It is working, if it removes these '' from around the values

# The number in square brackets in .values[1:] meand that we are choosing columns from nr. 1 till the last one
# First column is nr. 0, so this drops off the 'country' column.
# If you'd like to choose years 1960-1969, the command would be .values[1:11].

year = list(np.int_(energy_per_capita.columns.values[1:]))
print(year)

[1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]

# Prepare the data for y-axis.
# Insert the number of the row you are interested in into the square brackets, for Finland it is 51.

finland = energy_per_capita.loc[51]
finland = list(np.int_(finland[1:]))
print(finland)

[2200, 2250, 2360, 2480, 2680, 2890, 3070, 3130, 3340, 3670, 3870, 3940, 4190, 4510, 4380, 4180, 4460, 4520, 4660, 4960, 5150, 4930, 4810, 4820, 4910, 5270, 5480, 5960, 5590, 5750, 5690, 5740, 5380, 5620, 5970, 5660, 6070, 6280, 6320, 6280, 6260, 6410, 6730, 7080, 7130, 6560, 7110, 6970, 6670, 6270, 6830, 6540, 6280, 6120, 6210, 5920]

# All the data has been prepared, so we can create the plot with plt.plot(x,y) command

plt.plot(year, finland)
plt.title('Title')
plt.xlabel('x-axis label')
plt.ylabel('y-axis label')
plt.show()

If you want to add another line into the chart, here is the code:

Let’s compare the energy consumption between Finns and Indians.

# Finding India from the data set.

energy_per_capita.loc[energy_per_capita['country'] == 'India']

	country	1960	1961	1962	1963	1964	1965	1966	1967	1968	...	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
72	India	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	466	485	502	545	562	578	599	606	637	NaN

1 rows × 57 columns

# Prepare the second data for y-axis, in this case both have the same x-axis.

india = energy_per_capita.loc[72]
india = list(np.float_(india[1:]))
print(india)

[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 267.0, 267.0, 269.0, 273.0, 276.0, 280.0, 282.0, 279.0, 286.0, 286.0, 294.0, 298.0, 301.0, 306.0, 315.0, 319.0, 324.0, 334.0, 343.0, 350.0, 357.0, 363.0, 365.0, 371.0, 385.0, 389.0, 397.0, 399.0, 415.0, 417.0, 416.0, 421.0, 424.0, 440.0, 450.0, 466.0, 485.0, 502.0, 545.0, 562.0, 578.0, 599.0, 606.0, 637.0, nan]

# Plotting both lines, adding colors and names for the lines.

plt.plot(year, finland, color='blue', label='Finland')
plt.plot(year, india, color='red', label='India')

plt.title('Title')
plt.xlabel('x-axis label')
plt.ylabel('y-axis label')

plt.legend() # Prints names of the lines
plt.show()