We Keep Coding

Extract strings values from DataFrame column

I have the following DataFrame:
Student
food
1
R0100000
2
R0200000
3
R0300000
4
R0400000
I need to extract as a string the values of the "food" column of the df DataFrame when I filter the data.
For example, when I filter by the Student=1, I need the return value of "R0100000" as a string value, without any other characters or spaces.
This is the code to create the same DataFrame as mine:
data={'Student':[1,2,3,4],'food':['R0100000', 'R0200000', 'R0300000', 'R0400000']}
df=pd.DataFrame(data)
I tried to select the Dataframe Column and apply str(), but it does not return me the desired results:
df_new=df.loc[df['Student'] == 1]
df_new=df_new.food
df_str=str(df_new)
del df_new

This works for me:
s = df[df.Student==1]['food'][0]
s.strip()

It's pretty simple, first get the column.
like, col =data["food"] and then use col[index] to get respective value
So, you answer would be data["food"][0]
Also, you can use iloc and loc search for these.
(df.iloc[rows,columns], so we can use this property to get answer as, df.iloc[0,1])
df.loc[rows, column_names] example: df.loc[0,"food"]

Remove a dtype data from pandas dataframe column

I have a dataframe where it was added date and datetime information to a column where it was expected a string. What would be the best way to filter all dates and date values from a pandas dataframe column and replace those values to blank?
Thank you!

In general, if you provided a minimum working example of your problem, one could help more specifically, but assuming you have the following column:
df = pd.DataFrame(np.zeros(shape=(10,1)), columns = ["Mixed"])
df["Mixed"] = "foobar"
df.loc[2,"Mixed"] = pd.to_datetime("2022-08-22")
df.loc[7,"Mixed"] = pd.to_datetime("2022-08-21")
#print("Before Fix", df)
You can use apply(type) on the column to obtain the data-types of each cell and then use list comprehension [x!=str for x in types] to check for each cells datatype if its a string or not. After that, just replace those values that are not the desired datatype with a value of your choosing.
types = df["Mixed"].apply(type).values
mask = [x!=str for x in types]
df.loc[mask,"Mixed"] = "" #Or None, or whatever you want to overwrite it with
#print("After Fix", df)

Summary Row for a pd.DataFrame with multiindex

I have a multiIndex dataframe created with pandas similar to this one:
nest = {'A1': dfx[['aa','bb','cc']],
'B1':dfx[['dd']],
'C1':dfx[['ee', 'ff']]}
reform = {(outerKey, innerKey): values for outerKey, innerDict in nest.items() for innerKey, values in innerDict.items()}
dfzx = pd.DataFrame(reform)
What I am trying to achieve is to add a new row at the end of the dataframe that contains a summary of the total for the three categories represented by the new index (A1, B1, C1).
I have tried with df.loc (what I would normally use in this case) but I get error. Similarly for iloc.
a1sum = dfzx['A1'].sum().to_list()
a1sum = sum(a1sum)
b1sum = dfzx['B1'].sum().to_list()
b1sum = sum(b1sum)
c1sum = dfzx['C1'].sum().to_list()
c1sum = sum(c1sum)
totalcat = a1sum, b1sum, c1sum
newrow = ['Total', totalcat]
newrow
dfzx.loc[len(dfzx)] = newrow
ValueError: cannot set a row with mismatched columns
#Alternatively
newrow2 = ['Total', a1sum, b1sum, c1sum]
newrow2
dfzx.loc[len(dfzx)] = newrow2
ValueError: cannot set a row with mismatched columns
How can I fix the mistake? Or else is there any other function that would allow me to proceed?
Note: the DF is destined to be moved on an Excel file (I use ExcelWriter).
The type of results I want to achieve in the end is this one (gray row "SUM"

I came up with a sort of solution on my own.
I created a separate DataFrame in Pandas that contains the summary.
I used ExcelWriter to have both dataframes on the same excel worksheet.
Technically It would be then possible to style and format data in Excel (xlsxwriter or framestyle seem to be popular modules to do so). Alternatively one should be doing that manually.

Drop/edit rows in dataframe where entry doesn't meet condition

I know this has been asked before but I cannot find an answer that is working for me. I have a dataframe df that contains a column age, but the values are not all integers, some are strings like 35-59. I want to drop those entries. I have tried these two solutions as suggested by kite but they both give me AttributeError: 'Series' object has no attribute 'isnumeric'
df.drop(df[df.age.isnumeric()].index, inplace=True)
df = df.query("age.isnumeric()")
df = df.reset_index(drop=True)
Additionally is there a simple way to edit the value of an entry if it matches a certain condition? For example instead of deleting rows that have age as a range of values, I could replace it with a random value within that range.

Try with:
df.drop(df[df.age.str.isnumeric() == False].index, inplace=True)
If you check documentation isnumeric is a method of Series.str and not of Series. That's why you get that error.
Also you will need the ==False because you have mixed types and get a series with only booleans.

I'm posting it in case this also helps you with your last question. You can use pandas.DataFrame.at with pandas.DataFrame.Itertuples for iteration over rows of the dataframe and replace values:
for row in df.itertuples():
# iterate every row and change the value of that column
if row.age == 'non_desirable_value:
df.at[row.Index, "age"] = 'desirable_value'
Hence, it could be:
for row in df.itertuples():
if row.age.str.isnumeric() == False or row.age == 'non_desirable_value':
df.at[row.Index, "age"] = 'desirable_value'

Updating element of dataframe while referencing column name and row number

I am coming from an R background and used to being able to retrieve the value from a dataframe by using syntax like:
r_dataframe$some_column_name[row_number]
And I can assign a value to the dataframe by the following syntax:
r_dataframe$some_column_name[row_number] <= some_value
or without the arrow:
r_dataframe$some_column_name[row_number] = some_value
For example:
#create R dataframe data
employee <- c('John Doe','Peter Gynn','Jolie Hope')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2010-11-1','2008-3-25','2007-3-14'))
employ.data <- data.frame(employee, salary, startdate)
#print out the name of this employee
employ.data$employee[2]
#assign the name
employ.data$employee[2] <= 'Some other name'
I'm now learning some Python and from what I can see the most straight-forward way to retreive a value from a pandas dataframe is:
pandas_dataframe['SomeColumnName'][row_number]
I can see the similarities to R.
However, what confuses me is that when it comes to modifying/assigning the value in the pandas dataframe I need to completely change the syntax to something like:
pandas_dataframe.at[row_number, 'SomeColumnName'] = some_value
To read this code is going to require a lot more concentration because the column name and row number have changed order.
Is this the only way to perform this pair of operations? Is there a more logical way to do this that respects the consistent use of column name and row number order?

If I understand what you mean correctly, as #sammywemmy mentioned you can use .loc and .iloc to get/change value in any row and column.
If the order of your dataframe rows changes, you must define index to get every row (datapoint) by its index, even if the order has changed.
Like below:
df = pd.DataFrame(index=['a', 'b', 'c'], columns=['time', 'date', 'name'])
Now you can get the first row by its index:
df.loc['a'] # equivalent to df.iloc[0]

It turns out that pandas_dataframe.at[row_number, 'SomeColumnName'] can be used to modify AND retrieve information.