This is the presentation I was using in the pandas library meetup I delivered. More info about that meetup at https://www.meetup.com/life-michael/events/271737498/

1. The pandas Library
Haim Michael
September 8th
, 2020
All logos, trade marks and brand names used in this presentation belong
to the respective owners.
lifemichael
https://youtu.be/Go_6xXYEtkw

2. © 2008 Haim Michael 20150729
What is Pandas?
 Pandas is a fast, powerful, flexible and easy to use open
source data analysis and manipulation tool, built on top of
the Python programming language. (pandas.pydata.org)

3. © 2008 Haim Michael 20150729
Installing Pandas
 There are more than a few ways to install Pandas. The
simplest would be using the pip utility.
pip install pandas

4. © 2008 Haim Michael 20150729
Checking Pandas Version
 You can easily check the version of the Pandas library you
already have installed using the following code.
 The expected output should look like the following:

5. © 2008 Haim Michael 20150729
Importing Pandas
 In order to use Pandas we should first importing it. It is a
common practice to import it using the alias pd.
import pandas as pd

6. © 2008 Haim Michael 20150729
The DataFrame Class
 When instantiating the DataFrame class we will get an
object that represents a table.
import pandas as pd
df = pd.DataFrame({ "country":["israel","france","germany"],
"currency":["ils","euro","euro"],
"capitol":["jerusalem","paris","berlin"]
})
print(df)

7. © 2008 Haim Michael 20150729
The Series Class
 Each and every column of a DataFrame object is
represented using a Series object.
import pandas as pd
marks = pd.Series([88,90,72,64], name="Mark")
print(marks)

8. © 2008 Haim Michael 20150729
The describe() Function
 We can invoke this method both on a Series object and on a
DataFrame object. Calling this function we will get a detailed
statistic description.
import pandas as pd
marks = pd.Series([88,90,72,64], name="Mark")
print(marks.describe())

9. © 2008 Haim Michael 20150729
Reading & Writing Data
 The available methods allow us to read data from files in
various formats directly into a DataFrame object, and to
write data we already have in a DataFrame object directly to
files in various formats.

10. © 2008 Haim Michael 20150729
Reading & Writing Data
 The to_excel, to_csv, etc... are methods that we invoke
on a DataFrame object. These methods allow us to to write
the data we already have organized in a DataFrame object
to a new file of a specific format.
 The read_excel, read_csv, etc... are public methods that
were defined in the pandas module.

11. © 2008 Haim Michael 20150729
Writing to Excel Sample
import pandas as pd
df = pd.DataFrame({ "country":["israel","france","germany"],
"currency":["ils","euro","euro"],
"capitol":["jerusalem","paris","berlin"]
}
)
df.to_excel("countries.xlsx")

12. © 2008 Haim Michael 20150729
Reading from CSV
import pandas as pd
ob = pd.read_csv("countries.csv")
print(ob)

13. © 2008 Haim Michael 20150729
Selecting Column
 Selecting a column is done by using square brackets
together with the column name of the column of interest.
Each column is represented using an object of the type
Series.

14. © 2008 Haim Michael 20150729
Selecting Column
import pandas as pd
df = pd.DataFrame({ "country":["israel","france","germany"],
"currency":["ils","euro","euro"],
"capitol":["jerusalem","paris","berlin"]
}
)
countries = df["country"]
print(countries)
print(type(countries)

15. © 2008 Haim Michael 20150729
Selecting Columns
 Selecting multiple columns is done by using a list of column
names.

16. © 2008 Haim Michael 20150729
Selecting Columns
import pandas as pd
df = pd.DataFrame({ "country":["israel","france","germany"],
"currency":["ils","euro","euro"],
"capitol":["jerusalem","paris","berlin"]
}
)
ob = df[["country","capitol"]]
print(ob)
print(type(ob))

17. © 2008 Haim Michael 20150729
Selecting Rows
 Selecting rows based on a specific condition is done using a
condition we specify inside the selection brackets.

18. © 2008 Haim Michael 20150729
Selecting Rows
import pandas as pd
df = pd.DataFrame({ "first name":["moshe","daniel","tal"],
"last name":["israeli","cohen","lahat"],
"id":["234234","645645","678678"],
"average":[85,90,64]
}
)
beststudents = df[df["average"]>80]
print(beststudents)
print(type(beststudents))

19. © 2008 Haim Michael 20150729
Selecting Rows
 Selecting rows that a specific column in the selected row
has a value which is a specific value.

20. © 2008 Haim Michael 20150729
Selecting Rows
import pandas as pd
df = pd.DataFrame({ "first name":["moshe","daniel","tal","jane"],
"last name":["israeli","cohen","lahat","lala"],
"id":["234234","645645","678678","234234"],
"class":["1st","1st","2nd","3rd"]
}
)
premiumpassengers = df[(df["class"] == "1st") | (df["class"] == "3rd")]
print(premiumpassengers)
print(type(premiumpassengers))

21. © 2008 Haim Michael 20150729
Selecting Multiple Rows & Cols
 In order to select multiple rows and cols, a subset of our
data, we should use the iloc operator.

22. © 2008 Haim Michael 20150729
Selecting Multiple Rows & Cols
import pandas as pd
df = pd.DataFrame({ "first name":["moshe","daniel","tal"],
"last name":["israeli","cohen","lahat"],
"id":["234234","645645","678678"],
"class":["1st","1st","2nd"]
}
)
ob = df.iloc[0:2,0:2]
print(ob)
print(type(ob))