We Keep Coding

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)

Pandas dataframe : sample() function resets indexes?

Please consider a panda dataframe final_df with 142457 rows correctly indexed:
0
1
2
3
4
...
142452
142453
142454
142455
142456
I create / sample a new df data_test_for_all_models from this one:
data_test_for_all_models = final_df.copy().sample(frac=0.1, random_state=786)
A few indexes:
2235
118727
23291`
Now I drop rows from final_df with indexes in data_test_for_all_models :
final_df = = final_df.drop(data_test_for_all_models.index)
If I check a few indexes present in final_df :
final_df.iloc[2235]
returns wrongly a row.
I think it's a problem of reset indexes but which function does it: drop(), sample()?
Thanks.

You are using .iloc which provides integer-based indexing. You are getting the row number 2235, not the row with index 2235.
For that, you should use .loc:
final_df.loc[2235]
And you should get a KeyError.

Best way to move an unexpected column in a Pandas DF to a new DF?

Wondering what the best way to tackle this issue is. If I have a DF with the following columns
df1()
type_of_fruit name_of_fruit price
..... ..... .....
and a list called
expected_cols = ['name_of_fruit','price']
whats the best way to automate the check of df1 against the expected_cols list? I was trying something like
df_cols=df1.columns.values.tolist()
if df_cols != expected_cols:
And then try to drop to another df any columns not in expected_cols, but this doesn't seem like a great idea to me. Is there a way to save the "dropped" columns?
df2 = df1.drop(columns=expected_cols)
But then this seems problematic depending on column ordering, and also in cases where the columns could have either more values than expected, or less values than expected. In cases where there are less values than expected (ie the df1 only contains the column name_of_fruit) I'm planning on using
df1.reindex(columns=expected_cols)
But a bit iffy on how to do this programatically, and then how to handle the issue where there are more columns than expected.

You can use set difference using -:
Assuming df1 having cols:
In [542]: df1_cols = df1.columns # ['type_of_fruit', 'name_of_fruit', 'price']
In [539]: expected_cols = ['name_of_fruit','price']
In [541]: unwanted_cols = list(set(d1_cols) - set(expected_cols))
In [542]: df2 = df1[unwanted_cols]
In [543]: df1.drop(unwanted_cols, 1, inplace=True)

Use groupby along the columns axis to split the DataFrame succinctly. In this case, check whether the columns are in your list to form the grouper, and you can store the results in a dict where the True key gets the DataFrame with the subset of columns in the list and the False key has the subset of columns not in the list.
Sample Data
import pandas as pd
df = pd.DataFrame(data = [[1,2,3]],
columns=['type_of_fruit', 'name_of_fruit', 'price'])
expected_cols = ['name_of_fruit','price']
Code
d = dict(tuple(df.groupby(df.columns.isin(expected_cols), axis=1)))
# If you need to ensure columns are always there then do
#d[True] = d[True].reindex(expected_cols)
d[True]
# name_of_fruit price
#0 2 3
d[False]
# type_of_fruit
#0 1

Drop/edit rows in dataframe where entry doesn't meet condition

I know this has been asked before but I cannot find an answer that is working for me. I have a dataframe df that contains a column age, but the values are not all integers, some are strings like 35-59. I want to drop those entries. I have tried these two solutions as suggested by kite but they both give me AttributeError: 'Series' object has no attribute 'isnumeric'
df.drop(df[df.age.isnumeric()].index, inplace=True)
df = df.query("age.isnumeric()")
df = df.reset_index(drop=True)
Additionally is there a simple way to edit the value of an entry if it matches a certain condition? For example instead of deleting rows that have age as a range of values, I could replace it with a random value within that range.

Try with:
df.drop(df[df.age.str.isnumeric() == False].index, inplace=True)
If you check documentation isnumeric is a method of Series.str and not of Series. That's why you get that error.
Also you will need the ==False because you have mixed types and get a series with only booleans.

I'm posting it in case this also helps you with your last question. You can use pandas.DataFrame.at with pandas.DataFrame.Itertuples for iteration over rows of the dataframe and replace values:
for row in df.itertuples():
# iterate every row and change the value of that column
if row.age == 'non_desirable_value:
df.at[row.Index, "age"] = 'desirable_value'
Hence, it could be:
for row in df.itertuples():
if row.age.str.isnumeric() == False or row.age == 'non_desirable_value':
df.at[row.Index, "age"] = 'desirable_value'

Dataframe sum(axis=1) is returning Nan Values

I'm trying to make a sum of the second column ('ALL_PPA'), grouping by Numéro_département
Here's my code :
df.fillna(0,inplace=True)
df = df.loc[:, ('Numéro_département','ALL_PPA')]
df = df.groupby('Numéro_département').sum(axis=1)
print(df)
My DF is full of numbers, I don't have any NaN values, but when I apply the function df.sum(axis=1),some rows appear to have a NaN Value
Here's how my tab looks like before sum():
Here's after sum()
My question is : How am I supposed to do this? I've try to use numpy library but, it doesn't work as I want it to work

Drop the first row of that dataframe, as it just as the column names in it, and convert it to an int. Right now, it is an object because of the mixed data types:
df2 = df.iloc[1:].astype(int).copy()
Then, apply groupby.sum() and specify the column as well:
df3 = df2.groupby('Numero_department')['ALL_PPA'].sum()

I think using .dropna() before summing the DF will help remove any rows or columns (depending on the axis= you choose) with nan values. According to the screenshot provided, please drop the first line of the DF as it is a string.