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How to delete multiple rows in data frame at panda in python?

I am using pandas to make a dataframe. I want to delete 12 initial rows by drop function. every resources website says that you should use drop to delete the rows unfortunately it doesn't work. I don't know why. the error says that 'list' object has no attribute 'drop' could you do me a favor and find it what should I do?
url=Exp01.html
url=str(url)
df = pd.read_html(url)
df = df.drop(index=['1','12'],axis=0,inplace=True)
print(df)

You can slice the rows out:
df = df.loc[11:]
df
loc in general is configured this way:
df.loc[x:y]
where x is the starting index and y is the ending index.
[11:] gives starting index as 11 and no ending index

Pandas read_html returns a list of dataframes.
So df is a list on your example. First, take a look at what the list holds.
If it's just one table (dataframe), you can change it to:
df = pd.read_html(url)[0]
Full code:
url=Exp01.html
url=str(url)
df = pd.read_html(url)[0]
df.drop(index=df.index[:12], axis=0, inplace=True)

Is there a function for making a new dataframe using pandas to select only part of a word?

I am looking to select all values that include "hennessy" in the name, i.e. "Hennessy Black Cognac", "Hennessy XO". I know it would simply be
trial = Sales[Sales["Description"]if=="Hennessy"]
if I wanted only the value "Hennessy", but I want it if it contains the word "Hennessy" at all.
working on python with pandas imported
Thanks :)

You can use the in keyword to check if a value is present in a sequence.
Like this:
trial = "hennessy" in lower(Sales[Sales["Description"]])

you can try using str.startswith
import pandas as pd
# initialize list of lists
data = [['Hennessy Black Cognac', 10], ['Hennessy XO', 15], ['julian merger', 14]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age'])
new_df = df.loc[df.Name.str.startswith('Hennessy', na=False)]
new_df
or You can use apply to easily apply any string matching function to your column elementwise
df_new =df[df['Name'].apply(lambda x: x.startswith('Hennessy'))]
df_new

Using pd.Dataframe.replace with an apply function as the replace value

I have several dataframes that have mixed in some columns with dates in this ASP.NET format "/Date(1239018869048)/". I've figured out how to parse this into python's datetime format for a given column. However I would like to put this logic into a function so that I can pass it any dataframe and have it replace all the dates that it finds that match a regex using pd.Dataframe.replace.
something like:
def pretty_dates():
#Messy logic here
df.replace(to_replace=r'\/Date(d+)', value=pretty_dates(df), regex=True)
Problem with this is that the df that is being passed to pretty_dates is the whole dataframe not just the cell that is needed to be replaced.
So the concept I'm trying to figure out is if there is a way that the value that should be replaced when using df.replace can be a function instead of a static value.
Thank you so much in advance
EDIT
To try to add some clarity, I have many columns in a dataframe, over a hundred that contain this date format. I would like not to list out every single column that has a date. Is there a way to apply the function the clean my dates across all the columns in my dataset? So I do not want to clean 1 column but all the hundreds of columns of my dataframe.

I'm sure you can use regex to do this in one step, but here is how to apply it to the whole column at once:
df = pd.Series(['/Date(1239018869048)/',
'/Date(1239018869048)/'],dtype=str)
df = df.str.replace('\/Date\(', '')
df = df.str.replace('\)\/', '')
print(df)
0 1239018869048
1 1239018869048
dtype: object

As far as I understand, you need to apply custom function to selected cells in specified column. Hope, that the following example helps you:
import pandas as pd
df = pd.DataFrame({'x': ['one', 'two', 'three']})
selection = df.x.str.contains('t', regex=True) # put your regexp here
df.loc[selection, 'x'] = df.loc[selection, 'x'].map(lambda x: x+x) # do some logic instead
You can apply this procedure to all columns of the df in a loop:
for col in df.columns:
selection = df.loc[:, col].str.contains('t', regex=True) # put your regexp here
df.loc[selection, col] = df.loc[selection, col].map(lambda x: x+x) # do some logic instead

Need help to solve the Unnamed and to change it in dataframe in pandas

how set my indexes from "Unnamed" to the first line of my dataframe in python
import pandas as pd
df = pd.read_excel('example.xls','Day_Report',index_col=None ,skip_footer=31 ,index=False)
df = df.dropna(how='all',axis=1)
df = df.dropna(how='all')
df = df.drop(2)

To set the column names (assuming that's what you mean by "indexes") to the first row, you can use
df.columns = df.loc[0, :].values
Following that, if you want to drop the first row, you can use
df.drop(0, inplace=True)
Edit
As coldspeed correctly notes below, if the source of this is reading a CSV, then adding the skiprows=1 parameter is much better.

How to merge 2 columns in 1 within same dataframe (Python, Pandas)?

I'm following tutorial of Wes McKinney on using pandas/python for trading backtesting (http://youtu.be/6h0IVlp_1l8).
After pd.read_csv(...) he's using 'dt' (datetime) column as index of dataframe.
df.index = pd.to_datetime(df.pop('dt'))
However, my data has 2 separate columns, 'Date[G]' and 'Time[G]' and the data inside is something like 04-JAN-2013,00:00:00.000 (comma-separated).
How do i modify that line of code in order to do the same? I.e. merge two columns within one data frame, and then delete it. Or is there a way to do that during read_csv itself?
Thanks for all answers.

You should be able to concat two columns using apply() and then use to_datetime().
To remove columns from dataframe use drop() or just select columns you need:
df['dt'] = pd.to_datetime(df.apply(lambda x: x['Date[G]'] + ' ' + x['Time[G]'], 1))
df = df.drop(['Date[G]', 'Time[G]'], 1)
# ..or
# df = df[['dt', ...]]
df.set_index('dt', inplace = True)