I have the following dataframe:
df = pd.DataFrame({'TX':['bob','tim','frank'],'IL':['fred','bob','tim'],'NE':['tim','joe','bob']}) I would like to isolate the strings that occur across all columns to generate a list. The expected result is:
output = ['tim','bob'] The only way I can think to achieve this is using for loops which I would like to avoid. Is there a built-in pandas function suited to accomplishing this?
You can create mask for count values per columns and test if not missing values per rows by DataFrame.all:
m = df.apply(pd.value_counts).notna() print (m) TX IL NE bob True True True frank True False False fred False True False joe False False True tim True True True L = m.index[m.all(axis=1)].tolist() print (L) ['bob', 'tim'] You can achieve this by pandas.DataFrame.apply() and set.intersection(), like this:
cols_set = list(df.apply(lambda col: set(col.values)).values) output = list(set.intersection(*cols_set)) The result is following:
>>> print(output) ['tim', 'bob'] 
list(set.intersection(*[set(col) for col in df.values])). Summary of the above answer. Achieves the same result in lesser amount of code.
list(set.intersection(*[set(col) for col in df.values.T])). The author of the question is interested in values common for every column, not row! Next time, please, read the question carefully.df.T i.e. transpose of the data frame. Actual code would be list(set.intersection(*[set(col) for col in df.T.values]))IIUC,
you can stack all your columns vertically and then do a value_counts to count the occurrences of each item, we'll do that in the variable called s
we then want all occurrences of the names which are equal to the max number of occurrences, in this instance 3, the column values are now indices thanks to using stack
s = df.stack().value_counts() # or if you want to ignore duplicates column wise #df.stack().groupby(level=1).unique().explode().value_counts() print(s) tim 3 bob 3 frank 1 fred 1 joe 1 s1 = s[s.eq(s.max())].index.tolist() print(s1) ['tim', 'bob'] 
bob appeared twice in the first column, df.stack.value_counts() would give 4 for 'bob' and thus s1 would only return ['bob'] which is wrong according to the question.
df.stack().groupby(level=1).unique().explode().value_counts()