How to iterate over data frame dynamically?-CodePudding

I have a variable called columns_list which has a list of data frame columns desired: columns_list = ['col1','col2','col3']. How do I iterate through the dataframe dynamically?

Right now the code I have is like this:

for i in range(len(df)):
    s = tuple(zip(df[columns_list][0].str.split(",")[i],df[columns_list][1].str.split(",")[i],df[columns_list][2].str.split(",")[i])

How to make this work dynamically when the column_list keeps changing?

CodePudding user response：

Are you trying to do something like this?

for i in range(len(df)):
    s = []
    for col in columns_list:
        s.append(df.iloc[i][col].split(","))
    s = tuple(zip(s))
    ...

Or with list comprehensions:

for i in range(len(df)):
    s = tuple(zip(df.iloc[i][col].split(",") for col in columns_list))
    ...

CodePudding user response：

If you really want to iterate over the dataframe then I'd do something like:

for s in zip(*(df[c].str.split(",") for c in columns_list)):
    print(s)

Result for

df = pd.DataFrame(
    {"col1": ["a,b,c", "d,e"], "col2": ["1,2", "3,4,5"], "col3": ["x,y,z", "v"]}
)
columns_list = ["col2", "col3"]

(['1', '2'], ['x', 'y', 'z'])
(['3', '4', '5'], ['v'])

If you want to modify your original dataframe:

for c in columns_list:
    df[c] = df[c].str.split(",")

    col1       col2       col3
0  a,b,c     [1, 2]  [x, y, z]
1    d,e  [3, 4, 5]        [v]

Or if you want to build a new one with the splitted columns:

df_cols_splitted = pd.concat(
    (df[c].str.split(",") for c in columns_list), axis="columns"
)

        col2       col3
0     [1, 2]  [x, y, z]
1  [3, 4, 5]        [v]