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Python: Pandas Lesson 6

August 4, 2019 6 comments


Learning : Panda Lesson 6
Subject: Pass variable to df

In this post we will cover some commands that we will re-use in a coming post were we will start to develop a small system/app to do full managing of our zoo file. currently i am putting the BluePrint of the app, basically the menu will have these functions:
The Menu: [load, view, add-edit-delete, sort, missing data, save to csv …], so lets start..

If we want to add new row to our dataframe, first we need to know our columns name. In our case data file zoo here is the column we have:
( animal, id, water_need, supervisor, cage_no, years ), at this point we will do this as static variable, but in coming lesson we will use an interactive sample.
So, we will write a dictionary that hold our new_row and append it to the dataframe.

Add new row to the datafram
new_row={‘animal’:’Koala’,’id’:5555,’water_need’:99,’supervisor’:’na’,’cage_no’:55,’years’:10}
df=df.append([new_row])
print(‘\n\n The df after adding one new row.\n’,df)

You can see that there is a problem here, the id of our new row was entered manually (in this example) but we need this to be automatic and to do so we must first get the max value in the id column, add 1 to it then use it for the new entry. So we need to add this code:
next_id=df[‘id’].max() + 1
new_row={‘animal’:’koala’,’id’:next_id,’water_need’:99,’supervisor’:’na’,’cage_no’:55,’years’:10}



To delete a row, we simply re-define the df without that row we want to delete. Say we want to delete the row that has id = 1020. The id is a primary key in our data-set and there is no duplicated numbers in id column so we can use it to identify a specific row. I assume that we know that we read the datafram and we want to delete the row id number 1020, here is the code:

Delete the row with id = 1020
df=df[df.id !=1020]
print(‘\n\n df after deleting row id:1020\n’,df)




At the first paragraph i was talking about writing an app that fully manage the zoo file, so if the user want to read the rows based on a particular selection like : what are the animals in the cages number 2,5 and 8. Here is the code :

Animals in cage no 2,5 and 8
cage_arr=[2,5,8]
print(‘\n\n Animals in cages no.’,cage_arr,’\n’,df.loc[df[‘cage_no’].isin(cage_arr)])




:: Pandas Lessons Post ::

Lesson 1 Lesson 2 Lesson 3 Lesson 4
Lesson 5 Lesson 6 Lesson 7 Lesson 8
Lesson 9 Lesson 10 Lesson 11 Lesson 12



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Python: Pandas more commands



Learning : Pandas more commands
Subject: DataFrame Simple Statistics

In this post we will just go through some commands in pandas that related to simple statistics of dataframe, in coming table first we will list down all the commands then will see each in cation.

Function Description
1. sum() Get the sum of the value
2. cumsum() cumsum() is used to find the cumulative sumvalue over any axis. Each cell is populated with the cumulative sum of the values on upper cells.
3. count() counting the NaN in the DataFrame.
4. mean()
5. median
6. std() Standard deviation measures the spread of the data about the mean value.
7. max() Return the maximum value in each column.
8. min() Return the minimum value in each column.
9. prod() Return the value of product operation of the items in the column. (works only for number columns)
10. Cumprod() Return the number in the cell * all the cells over it.
11. abs() Returns the absolute value of the column and it is only applicable to numeric columns.
12. mode() It returns the most repeated value in the column.


1. sum() Using command as: print(df.sum()) it will return a sum of columns, if contain numaric then it will be a normal sum, if it is string return a string contain all as one string without spaces.
In our cas (data_file_zoo) the only practical result is the water-need where it represent the total amout of water we need in our zoo. So to get the sum of one column we can write it as: df[column name].sum()

print(‘\n\n Total amout of water we need is: ‘, df[‘water_need’].sum())


2. cumsum() Using command as: print(df.cumsum()) cumulative sumvalue will return the number in the cell + the sum of all the cells over it, we may need this function in a data analysis.


3. count() Using command as: print(df.count()), this function gives us the total data in each columns, so we know how many NaN or empty cells are in our table.


4. mean() Using command as: print(df.mean()), Thw Arithmetic Mean is the average of the numbers in the df for each columns.


5. median() Using command as: print(df.mediam()), in a sorted list
median will return the middle value, If there is an even number of items in the data set, then the median is the mean (average) of the two middlemost numbers. We can get the median of the specific Column.
# median of water_need column..
print(‘\n\n Median of the Water Need: ‘,df.loc[:,”water_need”].median()


6. sdt() Using command as: print(df.std()), it is the Standard deviation of the dataframe, or columns in the df.
The standard deviation measures the spread of the data about the mean value. It is useful in comparing sets of data which may have the same mean but a different range. In our example here (zoo file) some functions is not given the meaning that we may need, but if we have a data from statistical modeled or other scientific field this std() sure will be helpful.


7. max() Using command as: print(df.max()), return the maximum value in each column. If we want the max. value in a specific column then we use theis code:
print(‘\n\n’,df.loc[:,”water_need”].max())


8. min() Using command as: print(df.min()), same as max, the min() will return the minimum value in the df for each column, and we can get the min for only one column by using:
print(‘\n\n’,df.loc[:,”water_need”].min())

9. prod() Using command as: print(df.prod()), return the value of product operation of the items in the column. (works only for number columns)


10. cumprod() Using command as: print(df.cumprod()), cumulative product will return the number in the cell * all the cells over it.
To show this i will use a Series of numbers and apply cumprod.

cumprod()
some_value = pd.Series([2, 3, 5, -1, 2])
print(‘\n\n some_value in a column.\n’,some_value)
print(‘\n\n some_value.cumprod()\n’,some_value.cumprod())


11. abs() Using command as: print(df.abs()), It returns the absolute value of the column and it is only applicable to numeric columns.


12. mode() Using command as: print(df.mode()), It returns the most repeated value in the column.

Find the most repeated value:
print(‘\n\n Function: df.count()\n’,df.mode())

# mode of the specific column
df.loc[:,”animal”].mode()


:: Pandas Lessons Post ::

Lesson 1 Lesson 2 Lesson 3 Lesson 4
Lesson 5




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Python: Pandas Lesson 5

July 29, 2019 12 comments


Learning : Pandas Lesson 5
Subject: Columns rename and missing data.

We are still in the same track looking after commands that help us in managing and formatting the dataframe. In most cases, we will have a data file from the net, or from a source that may not consider formatting or standardization as his/their concern, or we may find a lots of missing data in the file. In this post we will go through some lines that will make the file in better shape.

Columns, First thing we will look at is the head of the data table. So First, check your data columns with this code:

print(‘\n\n Current columns of the data.\n’,df3.columns)

Now we have the a list of the columns in our datafile, and we can change any of them just to give a more clear meaning or any other purpose. I found that using rename method and passing new columns names as dictionary is better because we can rename without order also not stick to rename them all.

df.rename(columns={‘animal’:’Animal-Kind’,’id’:’ID’,’cage_no’:’InCage’}, inplace=True)
print(‘\n\n Table with new renaming columns .\n’,df3)

I just forgot to add .sample(6) so we will just have sample data, but anyway the new header is there and we use inplace = True so this new header will stay with us in df3.

Missing Data: This is the biggest challenge in any data file, some time the application that used to fills the form, or the person who entering the data or for any other reasons they are not handling the missing data in a standard way, so you may find just empty field, or ‘NA’ or dummy numbers like (0000), or (-0) or dashes (—). Handling such case is realy depending on the customer you are working for, like what they want to put/write in each empty field, now we are just talking about filling with standard key.

In coming code we are saying to pandas: whenever you found NaN replace it with ‘NA’
new_df=df3.fillna(‘NA’)
print(‘\n\n Replace NaN with NA.\n’new_df)



Note This: I will go to our data_file_zoo.csv and just add more NaN to some fields so our coming case will be meaningful.

Our data_file_zoo.csv has 6 columns, animal, id are primary keys and can’t be empty, so there MUST be filled. Now for the other columns I will say for each of columns if the data is NaN then we will replace it with:(MD:Missinf Data, NA:Not Available and – for numbers )
water_need : MD
supervisor : NA
InCage : –
years : –
Note That we MUST use the same columns name in the df we are working with.
Here is the code:

Replacing Missing Data
new_df=df2.fillna({‘water_need’:’MD’, ‘supervisor’:’NA’,’InCage’: ‘-‘ ,’years’:’-‘})
print(‘\n\n’,new_df)

(I mark the replacing fields.)

Let’s say we notes that some data in water_need column is not logical, like if we know it can’t be 600, so we just want to replace any number biger that or equal to 600 in that column ot ‘err’. Code here..

Code to change some value based on a condition.
df2.loc[df2[‘Water’] == 600, [‘Water’]] = ‘err’
print (‘\n\n change 600 to err./n’,df2)




:: Pandas Lessons Post ::

Lesson 1 Lesson 2 Lesson 3
Lesson 4 Lesson 5



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Python: Pandas Lesson 4

July 23, 2019 12 comments


Learning : Panda Lesson 4

Subject: DataFrame Columns: Hide, Drop, rename

We still workinng on dataframe and columns, we will go thrght some function and at the end I will just add a line to save the dataframe in a new CSV file. So let’s start.

We still working on our data_file_zoo.csv and here i am copying the column we have in the file or in our df.

print(‘\n\n Columns in thedataFrame..\n’,df.columns)



Now we have a list of columns in our DataFrame, some time we want to hide a column, here we will creat a variable and whenever we call this variable the column will not be shone on the screen.

Hide column ‘supervisor’
In this line we will set a variable to hide supervisor column, and just for sceen-shop we will present 6 random rows

hide_supervisor=df.drop([‘supervisor’], axis=1)
print(‘\n\n Sample data after hiding supervisor column\n’,hide_supervisors.sample(6))

In the upper case, we may have a password column or some key information column that we don’t want to be shown in the dataframe, then it’s good idea to create a DataFrame without this column an use it.

If we have a dataframe and we are examining some thing and don’t want to show all columns every time we print the df, so just show (say three) columns. To do this, first we will print the columns names so we know what we have in the df, then using coming code we will select whatever we want to show.

Show three columns frome the df, again we know the columns name so I will say:

animal_cage_years=df[[‘animal’,’cage_no’,’years’]]
print(‘\n\n Show selected Columns from df\n’,animal_cage_years.sample(6))

Now we will drop a column from the df, I will select ‘supervisor’, just like this:

Drop column name supervisor from the df.

print(‘\n\n Drop column ”supervisor” form the df’)
print(df.drop([‘supervisor’],axis=1))

To be Aware: In the above case, if we use the command on df and we add inplace=True then this will change the df, so any time we calling the df it will be without the ‘supervisor’ column. Here is the code..
df.drop([‘supervisor’], inplace=True, axis=1)
print(‘\n\n’,df)


If we want to hide more than one columns we just add them in the command like this:
hide_years_cages=df.drop([‘years’,’cage_no’], axis=1)
print(hide_years_cages.sample(6))

If we want to check wither or not a df contain column c_name if yes hide-it else print ‘Column not found’.

If column ‘cage_no’ in df hide it.
if ‘cage_no’ in df.columns:
hide_cage = df.drop([‘cage_no’], axis=1)
print(‘\n\n’,hide_cage.sample(6))
else:
print(‘Column not found’)

and we can in the else block just showing another dataframe.





:: Pandas Lessons Post ::

Lesson 1 Lesson 2 Lesson 3 Lesson 4
Lesson 5



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Python: Pandas Lesson

July 21, 2019 12 comments

Learning : DataFrame and some commands
Subject: Pandas printing selected rows

First thing we will do today, we will add another coloumn to our CSV data_file_zoo.csv, we will add ‘years’ this will be hwo old each animal in the zoo is.

File_Name: data_file_zoo.csv
animal,id,water_need,supervisor,cage_no,years
elephant,1001,500,Peter,5,5
elephant,1002,600,John,5,4
elephant,1003,550,Peter,5,4
tiger,1004,300,mark,4,8
tiger,1005,320,mark,4,9
tiger,1006,330,peter,3,5
tiger,1007,290,mark,3,3
tiger,1008,310,D.J,4,4
zebra,1009,200,D.J,8,
zebra,1010,220,D.J,9,8
zebra,1011,240,D.J,9,7
zebra,1012,230,mark,8,6
zebra,1013,220,D.J,8,3
zebra,1014,100,D.J,9,4
zebra,1015,80,peter,9,4
lion,1016,420,,1,9
lion,1017,600,D.J,1,8
lion,1018,500,,2,4
lion,1019,390,,2,5
kangaroo,1020,410,peter,7,8
kangaroo,1021,430,D.J,7,6
kangaroo,1022,410,mark,7,1


As we just update out file, we need to load it to the memory by calling the df (dataframe), this will happen once we run our code.
Here is a screen shot of the new data using print(df)



Lets say we want to know how many animals are numder 6 years. Here we will use df.loc to locate what we are looking for.

age_less_6 = df.loc[(dfyears<6)]
# To print we may use this:
print(‘ we have {} animals less than 6 years’.format(len(age_less_6)))

Now, we want to print only lion rows:
lino_rows = df.loc[(df.animal==’lion’)]



Here is only rows with animal name ‘elephants’:
elephant_rows=df.loc[(df.animal==’elephant’)]


Now let’s print only the rows with lion and elephants:
lion_and_elephant = df.loc[(df.animal==’lion’) | (df.animal == ‘elephant’)]


What if we want all the data but not the rows with lino or elephant.
all_exclude_lion_elephant=df.loc[(df.animal !=’lion’) & (df.animal !=’elephant’)]

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:: Pandas Lessons Post ::

Lesson 1 Lesson 2 Lesson 3 Lesson 4
Lesson 5



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Python: Triangle, Pentagonal, and Hexagonal 



Python: Triangle, Pentagonal, and Hexagonal 
Problem No.45 @ Projecteuler
Completed on: Thu, 11 Jul 2019, 21:31

Another straight-forward problem, in this task I create three functions each for Triangle, Pentagonal, and Hexagonal and we return the value of the formulas as been stated in the problem.

Using a for loop and a number range, I store the results in a list tn, pn, hn. then comparing the values in the three lists searching for same value.


The Code:


# P45
# Solved
# Completed on Thu, 11 Jul 2019, 21:31


def tn (n) :

return int(n*(n+1)/2)

def pn(n):

return int(n*(3*n-1)/2)

def hn (n):

return int(n*(2*n-1))

tn_list =[]
pn_list=[]
hn_list=[]

n = 0

# Notes: I run the code for large range, but to save more time after 5000 i select +10,000 each time.

for n in range (5000,60000):

tn_list.append(tn(n))

pn_list.append(pn(n))

hn_list.append(hn(n))

print ([x for x in tn_list if x in pn_list and x in hn_list])





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Python: Smallest Multiple



Python: Smallest multiple
Problem 5 @ projecteuler
Completed on: Thu, 4 Jul 2019, 22:30

Here I am quoting form ProjectEuler site:”

2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?”


So to solve this simple task all we need to loop through numbers and divide it by a list of (1,20) if yes return True otherwise return False and got to another number.
and so we done..



The Code:


codes here






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