[FIXED] Drop Dates greater a given date (keyerror)

Issue

I have a CSV file of following form:

Date	Data 1	Data 2	...	Data n
2010-01-02	123	222	...	223
2010-01-03	124	232	...	233
...	...	...	...	...
2021-11-06	424	332	...	133

I want to read all lines of this table into a Pandas dataframe where the column date is less than a given date, say 2010-01-05.

I just tried the following code:

df = pd.read_csv('test.csv')
df["Date"] = pd.to_datetime(daten["Date"], format="%Y-%m-%d")
df.drop(df["Date"] >= "2010-01-05", axis=0, inplace=True)
daten.set_index("Date", axis=0, inplace=True)

This gives me a key error

KeyError: '[ True True False ... False False False] not found in axis'

What is the right way to solve this problem?

Solution

drop method need an Index or column labels to drop not the rows themselves.

You can choose to keep rows that match condition:

df = pd.read_csv('test.csv', parse_dates=['Date'])
df = df[df['Date'] < "2010-01-05"]

Output:

>>> df
        Date  Data 1  Data 2  Data n
0 2010-01-02     123     222     223
1 2010-01-03     124     232     233

Or if you prefer use drop like this:

df = pd.read_csv('test.csv', parse_dates='Date')
df.drop(df[df["Date"] >= "2010-01-05"].index, inplace=True)

Answered By - Corralien