AttributeError 'dataframe' object has no attribute 'str'

Question

I am trying to filter out the dataframe that contains a list of product. However, I am getting the error 'dataframe' object has no attribute 'str' whenever I run the code.

Here is the line of code:

include_clique = log_df.loc[log_df['Product'].str.contains("Product A")]

Product is an object datatype.

import pandas as pd
import numpy as np

data = pd.read_csv("FILE.csv", header = None)

headerName = ["DRID", "Product", "M24", "M23", "M22", "M21"] 
data.columns = [headerName]

log_df = np.log(1 + data[["M24", "M23", "M22", "M21"]])
copy = data[["DRID", "Product"]].copy()
log_df = copy.join(log_df)

include_clique = log_df.loc[log_df['Product'].str.contains("Product A")]

Here is the head:

       ID  PRODUCT       M24       M23       M22  M21
0  123421        A  0.000000  0.000000  1.098612  0.0   
1  141840        A  0.693147  1.098612  0.000000  0.0   
2  212006        A  0.693147  0.000000  0.000000  0.0   
3  216097        A  1.098612  0.000000  0.000000  0.0   
4  219517        A  1.098612  0.693147  1.098612  0.0

Your code should work. Are you sure you are not doing log_df.str somewhere (instead of log_df['Product'].str)? Or maybe you have duplicated indexes with this name Product (e.g. two columns with same name) ? — rafaelc
– rafaelc, Commented Jul 24, 2018 at 15:19
@RafaelC yes I am positive. It was working yesterday, but now it is not working anymore. — David Luong
– David Luong, Commented Jul 24, 2018 at 15:20

hoang tran · Accepted Answer · 2018-09-28 12:02:38Z

28

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

edited Sep 28, 2018 at 12:02

answered Sep 28, 2018 at 9:24

hoang tran

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1 Comment

BigHeadEd Over a year ago

If entries in the column are NaN, you may still run into problems and require this answer's 2nd solution. I had an unexpected scenario where some of my df['column'] were single columns and others were DataFrames.

cottontail · Accepted Answer · 2023-02-02 19:20:23Z

You get AttributeError: 'DataFrame' object has no attribute ... when you try to access an attribute your dataframe doesn't have.

A common case is when you try to select a column using . instead of [] when the column name contains white space (e.g. 'col1 ').

df.col1       # <--- error
df['col1 ']   # <--- no error

Another common case is when you try to call a Series method on a DataFrame. For example, tolist() (or map()) are Series methods so they must be called on a column. If you call them on a DataFrame, you'll get

AttributeError: 'DataFrame' object has no attribute 'tolist'

AttributeError: 'DataFrame' object has no attribute 'map'

As hoang tran explains, this is what is happening with OP as well. .str is a Series accessor and it's not implemented for DataFrames.

Yet another case is if you have a typo and try to call/access an attribute that's simply not defined; e.g. if you try to call rows() instead of iterrows(), you'll get

AttributeError: 'DataFrame' object has no attribute 'rows'

You can check the full list of attributes using the following comprehension.

[x for x in dir(pd.DataFrame) if not x.startswith('_')]

When you assign column names as df.columns = [['col1', 'col2']], df is a MultiIndex dataframe now, so to access each column, you'll need to pass a tuple:

df['col1'].str.contains('Product A')    # <---- error
df['col1',].str.contains('Product A')   # <---- no error; note the trailing comma

In fact, you can pass a tuple to select a column of any MultiIndex dataframe, e.g.

df['level_1_colname', 'level_2_colname'].str.contains('Product A')

You can also flatten a MultiIndex column names by mapping a "flattener" function on it. A common one is ''.join:

df.columns = df.columns.map('_'.join)

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