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
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