Touch image to subscribe $2 per month

Learn Once and Never Forget Now Possible !

How to use Python matplotlib.pyplot


INVBAT.COM -A.I.
The Personal Memory Assistant Company

BECAUSE MOST OF US FORGET


AVAILABLE CALCULATOR FOR SUBSCRIPTION

import pandas as pd , import matplotlib.pyplot as plt
Part 5

In [1]:
# comment : import the pandas library
import pandas as pd
import matplotlib.pyplot as plt
# comment # do shift + enter
In [2]:
print('air_quality_all_data.xlsx file is located at directory  /Users/invbat/projects/air_quality_all_data.xlsx') 
print('Remember the directory path of your filename for example air_quality_all_data.xlsx. Otherwise you will get error message')
air_quality = pd.read_excel('/Users/invbat/projects/air_quality_all_data.xlsx')
air_quality.head()
# comment # do shift + enter

air_quality_all_data.xlsx file is located at directory  /Users/invbat/projects/air_quality_all_data.xlsx
Remember the directory path of your filename for example air_quality_all_data.xlsx. Otherwise you will get error message
Out[2]:
city country date.utc location parameter value unit
0 Antwerpen BE 2019-06-18 06:00:00+00:00 BETR801 pm25 18.0 µg/m³
1 Antwerpen BE 2019-06-17 08:00:00+00:00 BETR801 pm25 6.5 µg/m³
2 Antwerpen BE 2019-06-17 07:00:00+00:00 BETR801 pm25 18.5 µg/m³
3 Antwerpen BE 2019-06-17 06:00:00+00:00 BETR801 pm25 16.0 µg/m³
4 Antwerpen BE 2019-06-17 05:00:00+00:00 BETR801 pm25 7.5 µg/m³
In [3]:
# comment : I want to know the number of city with air quality station for my data analysis
air_quality.city.unique()
# comment # do shift + enter
Out[3]:
array(['Antwerpen', 'London', 'Paris'], dtype=object)
In [4]:
# comment : I want to work with the dates in the column datetime as datetime objects instead of plain
print('By applying the to_datetime function, pandas interprets the strings and convert these to datetime (i.e. datetime64[ns, UTC]) objects. ')
air_quality["datetime"] = pd.to_datetime(air_quality["date.utc"])
air_quality["datetime"].head()
# comment # do shift + enter

By applying the to_datetime function, pandas interprets the strings and convert these to datetime (i.e. datetime64[ns, UTC]) objects.
Out[4]:
0   2019-06-18 06:00:00+00:00
1   2019-06-17 08:00:00+00:00
2   2019-06-17 07:00:00+00:00
3   2019-06-17 06:00:00+00:00
4   2019-06-17 05:00:00+00:00
Name: datetime, dtype: datetime64[ns, UTC]
In [5]:
# comment : What is the start and end date of the time series data set working with?
print('The start date is the first row')
print('The end date is the second row')
print('')
air_quality["datetime"].min(), air_quality["datetime"].max()
# comment # Do shift + enter

The start date is the first row
The end date is the second row
Out[5]:
(Timestamp('2019-04-09 01:00:00+0000', tz='UTC'),
 Timestamp('2019-06-21 00:00:00+0000', tz='UTC'))
In [6]:
# comment : Now I want to know how many days of air quality monitoring our data has
air_quality["datetime"].max() - air_quality["datetime"].min()
# comment # Do shift + enter
Out[6]:
Timedelta('72 days 23:00:00')
In [7]:
# comment : I want to add a month column in the air_quality table
air_quality["month"] = air_quality["datetime"].dt.month
air_quality.head()
# comment # Do shift + enter
Out[7]:
city country date.utc location parameter value unit datetime month
0 Antwerpen BE 2019-06-18 06:00:00+00:00 BETR801 pm25 18.0 µg/m³ 2019-06-18 06:00:00+00:00 6
1 Antwerpen BE 2019-06-17 08:00:00+00:00 BETR801 pm25 6.5 µg/m³ 2019-06-17 08:00:00+00:00 6
2 Antwerpen BE 2019-06-17 07:00:00+00:00 BETR801 pm25 18.5 µg/m³ 2019-06-17 07:00:00+00:00 6
3 Antwerpen BE 2019-06-17 06:00:00+00:00 BETR801 pm25 16.0 µg/m³ 2019-06-17 06:00:00+00:00 6
4 Antwerpen BE 2019-06-17 05:00:00+00:00 BETR801 pm25 7.5 µg/m³ 2019-06-17 05:00:00+00:00 6
In [8]:
# comment : What is the average NO2 concentration for each day of the week for each of the measurement locations?
print('Assuming datetime 0 represent Sunday and datetime 6 represent Saturday')
air_quality.groupby( [air_quality["datetime"].dt.weekday, "location"])["value"].mean()
# comment # Do shit + enter

Assuming datetime 0 represent Sunday and datetime 6 represent Saturday
Out[8]:
datetime  location          
0         BETR801               25.065657
          FR04014               29.495417
          London Westminster    21.173077
1         BETR801               32.423077
          FR04014               34.402381
          London Westminster    26.102510
2         BETR801               18.812500
          FR04014               30.130579
          London Westminster    22.427039
3         BETR801               18.892857
          FR04014               28.749378
          London Westminster    21.354906
4         BETR801               18.180000
          FR04014               32.980851
          London Westminster    20.756930
5         BETR801               24.500000
          FR04014               24.955752
          London Westminster    19.367580
6         BETR801               27.297101
          FR04014               24.467917
          London Westminster    18.980349
Name: value, dtype: float64
In [9]:
# comment : Plot the typical NO2 pattern during the day of our time series of all stations together.
# In other words, what is the average value for each hour of the day?
fig1, axs = plt.subplots(figsize=(12, 4))
air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(kind='bar', rot=0, ax=axs)
#axs.set_ylabel("Average NO$_2$ concentration");
#axs.set_xlabel("Pollutant Every Hour For Each Day -  figure 1 - Bar Plot");
plt.ylabel("$NO_2 (µg/m^3)$");
plt.xlabel("Hour of the day");

fig2, axs = plt.subplots(figsize=(12, 4))
air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(kind='box', rot=0, ax=axs)
axs.set_ylabel("Average NO$_2$ concentration");
axs.set_xlabel("Distribution of Pollutant For Each Day -  figure 1 - Box Plot");

fig3, axs = plt.subplots(figsize=(12, 4))
air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(kind='line', rot=0, ax=axs)
axs.set_ylabel("Average NO$_2$ concentration");
axs.set_xlabel("Pollutant Every Hour For Each Day -  figure 3 - Line Plot");

fig4, axs = plt.subplots(figsize=(12, 6))
air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(kind='barh', rot=0, ax=axs)
axs.set_ylabel("Pollutant Every Hour For Each Day");
axs.set_xlabel("Average NO$_2$ concentration -  figure 4 - Horizontal Bar Plot");


# comment # Do shift + enter
In [10]:
# comment : Plot the typical NO2 average for each air quality station
fig1, axs = plt.subplots(figsize=(12, 4))
air_quality.groupby(air_quality["location"])["value"].mean().plot(kind='bar', rot=0, ax=axs)
#axs.set_ylabel("$NO_2 (µg/m^3)$");
#axs.set_xlabel("Group By Station -  figure 1 - bar Plot");
plt.ylabel("$NO_2 (µg/m^3)$");
plt.xlabel("Group By Station");
In [11]:
# comment : How many rows of data I am doing an analysis?
air_quality.shape
# comment # Do shift + enter
Out[11]:
(5272, 9)
In [12]:
# comment : What are the list of column fieldnames my data has?
air_quality.info()
# comment # Do shift + enter

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5272 entries, 0 to 5271
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   city       5272 non-null   object             
 1   country    5272 non-null   object             
 2   date.utc   5272 non-null   object             
 3   location   5272 non-null   object             
 4   parameter  5272 non-null   object             
 5   value      5272 non-null   float64            
 6   unit       5272 non-null   object             
 7   datetime   5272 non-null   datetime64[ns, UTC]
 8   month      5272 non-null   int64              
dtypes: datetime64[ns, UTC](1), float64(1), int64(1), object(6)
memory usage: 370.8+ KB
In [13]:
# comment : Get me the average or mean of pollutant no2, and pm25 for each location of air quality station
air_quality.pivot_table(values="value", index="location", columns="parameter", aggfunc="mean", margins=False)
# comment # Do shift + enter
Out[13]:
parameter no2 pm25
location
BETR801 26.950920 23.169492
FR04014 29.374284 NaN
London Westminster 29.740050 13.443568
In [14]:
# comment : Get me the overall average or mean of pollutant no2, and pm25 for each location of air quality station
air_quality.pivot_table(values="value", index="location", columns="parameter", aggfunc="mean", margins=True)
# comment # Do shift + enter
Out[14]:
parameter no2 pm25 All
location
BETR801 26.950920 23.169492 24.982353
FR04014 29.374284 NaN 29.374284
London Westminster 29.740050 13.443568 21.491708
All 29.430316 14.386849 24.222743
In [ ]:

PREVIOUS LESSON 7 NEXT LESSON 9





Open More Calculator Database



Why do you need a personal augmented intelligence (AI) chatbot? Because it is useful in knowledge storage, information retrieval, and fast computation with less error.


IN-V-BAT-AI uses explainable Artificial Intelligence (AI) to automate repetitive solved problem or routine calculation so we can focus our brain power to solve harder new problem then automate again once it is solved.


Learn Once and Never Forget
Now Possible !




IN-V-BAT-AI
USED IN 207 COUNTRIES

Organic Growth



INVBAT.COM - A.I. is a disruptive innovation in computing and web search technology. For example scientific calculator help us speed up calculation but we still need to remember accurately the formula and the correct sequence of data entry. Here comes the disruptive innovation from INVBAT.COM-A.I. , today the problem of remembering formula and the correct sequence of data entry is now solved by combining formula and calculation and make it on demand using smartphone, tablet, notebook, Chromebook, laptop, desktop, school smartboard and company big screen tv in conference room with internet connection.