Develop Reference

Loop over a column in a Dataframe and then replace with a list of data

I have a dataframe that has "?" in the data.This is a pic of said df I had to calculate the values that would go where these ? are in another part of the assignment.values that need to be added. I need a way to add these values into a SINGLE column in a pandas dataframe. I need it to add the data in the order that it is in the first picture (even though the indices obviously do not match) in order of the "?" down the larger column in the main dataframe.

Using describe() method to exclude a column

I am new to using python with data sets and am trying to exclude a column ("id") from being shown in the output. Wondering how to go about this using the describe() and exclude functions.

describe works on the datatypes. You can include or exclude based on the datatype & not based on columns. If your column id is of unique data type, then
df.describe(exclude=[datatype])
or if you just want to remove the column(s) in describe, then try this
cols = set(df.columns) - {'id'}
df1 = df[list(cols)]
df1.describe()
TaDa its done. For more info on describe click here

You can do that by slicing your original DF and remove the 'id' column. One way is through .iloc . Let's suppose the column 'id' is the first column from you DF, then, you could do this:
df.iloc[:,1:].describe()
The first colon represents the rows, the second the columns.

Although somebody responded with an example given from the official docs which is more then enough, I'd just want to add this, since It might help a few ppl:
IF your DataFrame is large (let's say 100s columns), removing one or two, might not be a good idea (not enough), instead, create a smaller DataFrame holding what you're interested and go from there.
Example of removing 2+ columns:
table_of_columns_you_dont_want = set(your_bigger_data_frame.colums) = {'column_1', 'column_2','column3','etc'}
your_new_smaller_data_frame = your_new_smaller_data_frame[list[table_of_columns_you_dont_want]]
your_new_smaller_data_frame.describe()
IF your DataFrame is medium/small size, you already know every column and you only need a few columns, just create a new DataFrame and then apply describe():
I'll give an example from reading a .csv file and then read a smaller portion of that DataFrame which only holds what you need:
df = pd.read_csv('.\docs\project\file.csv')
df = [['column_1','column_2','column_3','etc']]
df.describe()

Use output.describe(exclude=['id'])

pandas max function results in inoperable DataFrame

I have a DataFrame with four columns and want to generate a new DataFrame with only one column containing the maximum value of each row.
Using df2 = df1.max(axis=1) gave me the correct results, but the column is titled 0 and is not operable. Meaning I can not check it's data type or change it's name, which is critical for further processing. Does anyone know what is going on here? Or better yet, has a better way to generate this new DataFrame?

It is Series, for one column DataFrame use Series.to_frame:
df2 = df1.max(axis=1).to_frame('maximum')

Removing rows from a dataframe, if the observation for a specific variable is numeric

data example I have a large data frame with over 20000 observations, I have a variable called “station” and I need to remove all rows that only have numbers as the s station name.
The only code that has worked so far is :
Df[‘station’][~df[‘station’].str.isnumeric()
However this only creates a data frame with one variable

You can use an extre column with .str.isnumeric() to be used later on as a filter:
df['filter'] = df['station'].str.isnumeric()
df_filtered = df[df['filter'] != False]#.drop(columns=['filter']
This should return all rows that are not only numbers for the column station. After that, you can remove the hash if you wish to drop the filter column to mantain you original structure.

You can do it like so,
df_filtered = df[df['station'].str.isnumeric()==False]
You wouldn't have to do set operations on your dataframe if you use this.
The internal statements are ultimately a Boolean logic filter that is being applied on the dataframe.

Merge dataframe and string

I am trying to use a data frame to regroup different kind of data.
I have a data frame with 3 columns :
one that I define and the index (used a groupby command)
one that regroups a parameter, say 'valeur1', for which I want a mean for these that have the same index (used a mean command after the group by)
the last column contains strings. There is only 1 string for each index but some cell might contain nan.
I am trying to get in the end a dataframe with the mean for 1 parameter depending on the index as well as the string that goes with the index (nan in the string column are not important). Here is a picture with an example or what I am trying to get : illustration . Main issue is that dataframe.mean does not work with string
The code I used so far is pretty basic :
dataRaw=pd.read_csv('file.csv', sep=';', encoding='latin-1')
data=dataRaw.groupby(index)
databis=data.mean();
Any suggestion would be greatly appreciated.
Thanks !

I think you need to group by multiple columns:
databis = dataRaw.groupby(['index', 'String']).mean()