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Iterate over a pandas DataFrame & Check Row Comparisons

I'm trying to iterate over a large DataFrame that has 32 fields, 1 million plus rows.
What i'm trying to do is iterate over each row, and check whether any of the rest of the rows have duplicate information in 30 of the fields, while the other two fields have different information.
I'd then like to store the the ID info. of the rows that meet these conditions.
So far i've been trying to figure out how to check two rows with the below code, it seems to work when comparing single columns but throws an error when I try more than one column, could anyone advise on how best to approach?
for index in range(len(df)):
for row in range(index, len(df)):
if df.iloc[index][1:30] == df.iloc[row][1:30]:
print(df.iloc[index])

As a general rule, you should always always try not to iterate over the rows of a DataFrame.
It seems that what you need is the pandas duplicated() method. If you have a list of the 30 columns you want to use to determine duplicates rows, the code looks something like this:
df.duplicated(subset=['col1', 'col2', 'col3']) # etc.
Full example:
# Set up test df
from io import StringIO
sub_df = pd.read_csv(
StringIO("""ID;col1;col2;col3
One;23;451;42;31
Two;24;451;42;54
Three;25;513;31;31"""
),
sep=";"
)
Find which rows are duplicates in col1 and col2. Note that the default is that the first instance is not marked as a duplicate, but later duplicates are. This behaviour can be changed as described in the documentation I linked to above.
mask = sub_df.duplicated(["col1", "col2"])
This looks like:
Now, filter using the mask.
sub_df["ID"][sub_df.duplicated(["col1", "col2"])]
Of course, you can do the last two steps in one line.

EDA for loop on multiple columns of dataframe in Python

Just a random q. If there's a dataframe, df, from the Boston Homes ds, and I'm trying to do EDA on a few of the columns, set to a variable feature_cols, which I could use afterwards to check for na, how would one go about this? I have the following, which is throwing an error:
This is what I was hoping to try to do after the above:
Any feedback would be greatly appreciated. Thanks in advance.

There are two problems in your pictures. First is a keyError, because if you want to access subset of columns of a dataframe, you need to pass the names of the columns in a list not a tuple, so the first line should be
feature_cols = df[['RM','ZN','B']]
However, this will return a dataframe with three columns. What you want to use in the for loop can not work with pandas. We usually iterate over rows, not columns, of a dataframe, you can use the one line:
df.isna().sum()
This will print all names of columns of the dataframe along with the count of the number of missing values in each column. Of course, if you want to check only a subset of columns, you can. replace df buy df[list_of_columns_names].

You need to store the names of the columns only in an array, to access multiple columns, for example
feature_cols = ['RM','ZN','B']
now accessing it as
x = df[feature_cols]
Now to iterate on columns of df, you can use
for column in df[feature_cols]:
print(df[column]) # or anything
As per your updated comment,. if your end goal is to see null counts only, you can achieve without looping., e.g
df[feature_cols].info(verbose=True,null_count=True)

How to append rows to a Pandas dataframe, and have it turn multiple overlapping cells (with the same index) into a single value, instead of a series?

I am appending different dataframes to make one set. Occasionally, some values have the same index, so it stores the value as a series. Is there a quick way within Pandas to just overwrite the value instead of storing all the values as a series?

You weren't very clear guy. If you want to resolve the duplicated indexes problem, probably the pd.Dataframe.reset_index() method will be enough. But, if you have duplicate rows when you concat the Dataframes, just use the pd.DataFrame.drop_duplicates() method. Else, share a bit of your code with or be clearer.

I'm not sure that the code below is what you're searching.
we say two dataframes, one columns, the same index and different values. and you wanna overwrite the value in one dataframe with the other. you can do it with a simple loop with iloc indexer.
import pandas as pd
df_1 = pd.DataFrame({'col_1':['a','b','c','d']})
df_2 = pd.DataFrame({'col_1':['q','w','e','r']})
rows = df_1.shape[0]
for idx in range(rows):
df_1['col_1'].iloc[idx] = df_2['col_2'].iloc[idx]
Then, you check the df_1. you should get that:
df_1
col_1
0 q
1 w
2 e
3 r
Whatever the response is what you want, let me know so I can help you.

python pandas difference between df_train["x"] and df_train[["x"]]

I have the following dataset and reading it from csv file.
x =[1,2,3,4,5]
with the pandas i can access the array
df_train = pd.read_csv("train.csv")
x = df_train["x"]
And
x = df_train[["x"]]
I could wonder since both producing the same result the former one could make sense but later one not. PLEASE, COULD YOU explain the difference and use?

In pandas, you can slice your data frame in different ways. On a high level, you can choose to select a single column out of a data frame, or many columns.
When you select many columns, you have to slice using a list, and the return is a pandas DataFrame. For example
df[['col1', 'col2', 'col3']] # returns a data frame
When you select only one column, you can pass only the column name, and the return is just a pandas Series
df['col1'] # returns a series
When you do df[['col1']], you return a DataFrame with only one column. In other words, it's like your telling pandas "give me all the columns from the following list:" and just give it a list with one column on it. It will filter your df, returning all columns in your list (in this case, a data frame with only 1 column)
If you want more details on the difference between a Series and a one-column DataFrame, check this thread with very good answers

How to add values from one dataframe into another ignoring the row indices

I have a pandas dataframe called trg_data to collect data that I am producing in batches. Each batch is produced by a sub-routine as a smaller dataframe df with the same number of columns but less rows and I want to insert the values from df into trg_data at a new row position each time.
However, when I use the following statement df is always inserted at the top. (i.e. rows 0 to len(df)).
trg_data.iloc[trg_pt:(trg_pt + len(df))] = df
I'm guessing but I think the reason may be that even though the slice indicates the desired rows, it is using the index in df to decide where to put the data.
As a test I found that I can insert an ndarray at the right position no problem:
trg_data.iloc[trg_pt:(trg_pt + len(df))] = np.ones(df.shape)
How do I get it to ignore the index in df and insert the data where I want it? Or is there an entirely different way of achieving this? At the end of the day I just want to create the dataframe trg_data and then save to file at the end. I went down this route because there didn't seem to be a way of easily appending to an existing dataframe.
I've been working at this for over an hour and I can't figure out what to google to find the right answer!

I think I may have the answer (I thought I had already tried this but apparently not):
trg_data.iloc[trg_pt:(trg_pt + len(df))] = df.values
Still, I'm open to other suggestions. There's probably a better way to add data to a dataframe.

The way I would do this is save all the intermediate dataframes in an array, and then concatenate them together
import pandas as pd
dfs = []
# get all the intermediate dataframes somehow
# combine into one dataframe
trg_data = pd.concatenate(dfs)

Both
trg_data = pd.concat([df1, df2, ... dfn], ignore_index=True)
and
trg_data = pd.DataFrame()
for ...: #loop that generates df
trg_data = trg_data.append(df, ignore_index=True) #you can reuse the name df
shoud work for you.