Short answer: change data.columns=[headerName] into data.columns=headerName

Explanation: when you set data.columns=[headerName], the columns are MultiIndex object. Therefore, your log_df['Product'] is a DataFrame and for DataFrame, there is no str attribute.

When you set data.columns=headerName, your log_df['Product'] is a single column and you can use str attribute.

For any reason, if you need to keep your data as MultiIndex object, there is another solution: first convert your log_df['Product'] into Series. After that, str attribute is available.

products = pd.Series(df.Product.values.flatten())
include_clique = products[products.str.contains("Product A")]

However, I guess the first solution is what you're looking for

Short answer: change data.columns=[headerName] into data.columns=headerName

Explanation: when you set data.columns=[headerName], the columns are MultiIndex object. Therefore, your log_df['Product'] is a DataFrame and for DataFrame, there is no str attribute.

When you set data.columns=headerName, your log_df['Product'] is a single column and you can use str attribute.

For any reason, if you need to keep your data as MultiIndex object, there is another solution: first convert your log_df['Product'] into Series. After that, str attribute is available.

products = pd.Series(df.Product.values.flatten())
include_clique = products[products.str.contains("Product A")]

However, I guess the first solution is what you're looking for

The error means exactly what it says:

AttributeError: 'str' object has no attribute 'DataFrame' 
      ^           ^                                ^
the kind of error |                                |
       the thing you tried to use      what was missing from it

The line it's complaining about:

df = pd.DataFrame(date, columns = ['Date'])
     ^      ^
     |   the attribute the error said was missing
the thing the error said was a string

has been working no problem until I added a few lines of code above

Evidently, somewhere in the "few lines of code above", you caused pd to be a string. And sure enough, when we look at those few lines of code, we find:

pd = PDays[j]
^       ^
|    the string that you're making it into
the thing that you're making a string

This could be because your dataframe has multiple columns with the same name. I can recreate this error by doing the following:

import pandas as pd

df = pd.DataFrame([['0','1','2'],['2','4','5']],columns = ['a','b','b'])
for c in df.columns:
    print(df[c].str.replace("1",""))

The problem is that once you get to the repeated column name (in my example, when c == 'b'), then df[c] is actually a dataframe with 2 cols and .str is not available.

So if this is your issue, find the columns with the same name and give them unique names.

Also, as mentioned by @JonClements it isn't necessary to loop over the columns at all, you can just do df = df.replace('.*0.*', '', regex=True)

value_counts is a Series method rather than a DataFrame method (and you are trying to use it on a DataFrame, clean). You need to perform this on a specific column:

clean[column_name].value_counts()

It doesn't usually make sense to perform value_counts on a DataFrame, though I suppose you could apply it to every entry by flattening the underlying values array:

pd.value_counts(df.values.flatten())

"sklearn.datasets" is a scikit package, where it contains a method load_iris().

load_iris(), by default return an object which holds data, target and other members in it. In order to get actual values you have to read the data and target content itself.

Whereas 'iris.csv', holds feature and target together.

FYI: If you set return_X_y as True in load_iris(), then you will directly get features and target.

from sklearn import datasets
data,target = datasets.load_iris(return_X_y=True)

You need to use apply:

DataFrame[['ColumnA','ColumnB']].apply(lambda s: s.str.split('\:').str[-1].str.strip())

Or a list comprehension, which can be significantly faster as chaining str methods can be slow:

[[s.split('\:')[-1].strip() for s in df[col]] for col in ['ColumnA','ColumnB']]

Using df.apply

pd.Series.str.replace is a series method not for dataframes. You can use apply on each column (series) instead.

dataFrame[headers] = dataFrame[headers].apply(lambda x: x.str.replace(',', ''))

Another option is to use apply on each row (series) with axis=1.

Using df.applymap

Or, you can use applymap and treat each cell as a string and use replace directly on them.

dataFrame[headers] = dataFrame[headers].applymap(lambda x: x.replace(',', ''))

Using df.replace

You can also use df.replace which is a method available to replace values in df directly across all columns selected. But, for this purpose you will have to set regex=True.

dataFrame[headers] = dataFrame[headers].replace(',', '', regex=True)

First problem shoud be duplicated columns names, so after select colB get not Series, but DataFrame:

df = pd.DataFrame([['Example: s', 'as', 2], ['dd', 'aaa', 3]], columns=['colB','colB','colC'])
print (df)
         colB colB  colC
0  Example: s   as     2
1          dd  aaa     3

print (df['colB'])
         colB colB
0  Example: s   as
1          dd  aaa

#print (df['colB'].str.contains('Example:'))
#>AttributeError: 'DataFrame' object has no attribute 'str'

Solution should be join columns together:

print (df['colB'].apply(' '.join, axis=1))
0    Example: s as
1           dd aaa

df['colB'] = df.pop('colB').apply(' '.join, axis=1)
df = df[~df['colB'].str.contains('Example:')] 
print (df)
   colC    colB
1     3  dd aaa

Second problem should be hidden MultiIndex:

df = pd.DataFrame([['Example: s', 'as', 2], ['dd', 'aaa', 3]], columns=['colA','colB','colC'])
df.columns = pd.MultiIndex.from_arrays([df.columns])
print (df)
         colA colB colC
0  Example: s   as    2
1          dd  aaa    3

print (df['colB'])
  colB
0   as
1  aaa

#print (df['colB'].str.contains('Example:'))
#>AttributeError: 'DataFrame' object has no attribute 'str'

Solution is reassign first level:

df.columns = df.columns.get_level_values(0)
df = df[~df['colB'].str.contains('Example:')] 
print (df)
         colA colB  colC
0  Example: s   as     2
1          dd  aaa     3

And third should be MultiIndex:

df = pd.DataFrame([['Example: s', 'as', 2], ['dd', 'aaa', 3]], columns=['colA','colB','colC'])
df.columns = pd.MultiIndex.from_product([df.columns, ['a']])
print (df)
         colA colB colC
            a    a    a
0  Example: s   as    2
1          dd  aaa    3

print (df['colB'])
     a
0   as
1  aaa

print (df.columns)
MultiIndex(levels=[['colA', 'colB', 'colC'], ['a']],
           codes=[[0, 1, 2], [0, 0, 0]])

#print (df['colB'].str.contains('Example:'))
#>AttributeError: 'DataFrame' object has no attribute 'str'

Solution is select MultiIndex by tuple:

df1 = df[~df[('colB', 'a')].str.contains('Example:')] 
print (df1)
         colA colB colC
            a    a    a
0  Example: s   as    2
1          dd  aaa    3

Or reassign back:

df.columns = df.columns.get_level_values(0)
df2 = df[~df['colB'].str.contains('Example:')] 
print (df2)
         colA colB  colC
0  Example: s   as     2
1          dd  aaa     3

Or remove second level:

df.columns = df.columns.droplevel(1)
df2 = df[~df['colB'].str.contains('Example:')] 
print (df2)
         colA colB  colC
0  Example: s   as     2
1          dd  aaa     3