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pandas.to_datetime() does not filter when used with loc[] and comparison operator

I downloaded a .csv file to do some practice, a column named "year_month" is string with the format "YYYY-MM"
By doing:
df = pd.read_csv('C:/..../migration_flows.csv',parse_dates=["year_month"])
"year_month" is Dtype=object. So far so good.
By doing:
df["year_month"] = pd.to_datetime(df["year_month"],format='%Y-%m-%d')
it is converted to daterime64[ns]. So far so good.
I try to filter certain dates by doing:
filtered_df = df.loc[(df["year_month"]>= pd.Timestamp(2018-1-1))]
The program returns the whole column as if nothing happened. For instance, it starts displaying, starting from the date "2001-01-01"
Any thoughts on how to filter properly? Many thanks

how about this
df.loc[(df["year_month"]>= pd.to_datetime('2018-01-01'))]
or
df.loc[(df["year_month"]>= pd.Timestamp('2018-01-01'))]

Conversion of string to float

I read from the following file my data, and create a table.
tracks=pd.read_csv('C:\\Users\\demet\\Desktop\\Internship\\scripts\\tracks-rainy.csv')
Yet when I print for instance an element instead of obtaining a float a get a string.
print(tracks.iloc[track_id][3][0])
What should I add to my project.

You can try:
tracks=pd.read_csv('C:\\Users\\demet\\Desktop\\Internship\\scripts\\tracks-rainy.csv', dtype={'track_id':'Float64'})
Which tell pandas to interpret the column as Float. (As Karl Knechtel said)

If you do not want to initiate the conversion when reading the csv file, you can always do list comprehension with a float conversion.
tracks['track_id'] = [float(i) for i in tracks['track_id']]

Convert column in df to float (sounds simple)

Am fairly new to code but have managed to solve most problems, here though I am stuck. I have a column in a df where all the values are a string in brackets for example '[0.0987]', I can't seem to convert these to float in order to calculate the mean. Every method results in an error such as: 'could not convert string to float:' or 'Could not convert to numeric'. Can't share a link so image below shows an example csv I am loading into pandas.

You have to strip the brackets from the values.
df["qout"].str.strip('[]').astype(float)
Strip - will remove the [] from the column
astype - Will typecast the data as float

You probably have to strip the brackets. Does this work? qout_as_float = float(qout[1:-1])

not able to change object to float in pandas dataframe

just started learning python. trying to change a columns data type from object to float to take out the mean. I have tried to change [] to () and even the "". I dont know whether it makes a difference or not. Please help me figure out what the issue is. thanks!!
My code:
df["normalized-losses"]=df["normalized-losses"].astype(float)
error which i see: attached as imageenter image description here

Use:
df['normalized-losses'] = df['normalized-losses'][~(df['normalized-losses'] == '?' )].astype(float)
Using df.normalized-losses leads to interpreter evaluating df.normalized which doesn't exist. The statement you have written executes (df.normalized) - (losses.astype(float)).There appears to be a question mark in your data which can't be converted to float.The above statement converts to float only those rows which don't contain a question mark and drops the rest.If you don't want to drop the columns you can replace them with 0 using:
df['normalized-losses'] = df['normalized-losses'].replace('?', 0.0)
df['normalized-losses'] = df['normalized-losses'].astype(float)

Welcome to Stack Overflow, and good luck on your Python journey! An important part of coding is learning how to interpret error messages. In this case, the traceback is quite helpful - it is telling you that you cannot call normalized after df, since a dataframe does not have a method of this name.
Of course you weren't trying to call something called normalized, but rather the normalized-losses column. The way to do this is as you already did once - df["normalized-losses"].
As to your main problem - if even one of your values can't be converted to a float, the columnwide operation will fail. This is very common. You need to first eliminate all of the non-numerical items in the column, one way to find them is with df[~df['normalized_losses'].str.isnumeric()].

The "df.normalized-losses" does not signify anything to python in this case. you can replace it with df["normalized-losses"]. Usually, if you try
df["normalized-losses"]=df["normalized-losses"].astype(float)
This should work. What this does is, it takes normalized-losses column from dataframe, converts it to float, and reassigns it to normalized column in the same dataframe. But sometimes it might need some data processing before you try the above statement.

You can't use - in an attribute or variable name. Perhaps you mean normalized_losses?

How can I read every field as string in xlwings?

I have an exelfile that I want to convert but the default type for numbers is float. How can I change it so xlwings explicitly uses strings and not numbers?
This is how I read the value of a field:
xw.Range(sheet, fieldname ).value
The problem is that numbers like 40 get converted to 40.0 if I create a string from that. I strip it with: str(xw.Range(sheetFronius, fieldname ).value).rstrip('0').rstrip('.') but that is not very helpful and leads to errors because sometimes the same field can contain both a number and a string. (Not at the same time, the value is chosen from a list)

With xlwings if no options are set during reading/writing operations single cells are read in as 'floats'. Also, by default cells with numbers are read as 'floats'. I scoured the docs, but don't think you can convert a cell that has numbers to a 'string' via xlwings outright. Fortunately all is not lost...
You could read in the cells as 'int' with xlwings and then convert the 'int' to 'string' in Python. The way to do that is as follows:
xw.Range(sheet, fieldname).options(numbers=int).value
And finally, you can read in your data this way (by packing the string conversion into the options upfront):
xw.Range(sheet, fieldname).options(numbers=lambda x: str(int(x))).value
Then you would just convert that to string in Python in the usual way.
Good luck!

In my case conclusion was, just adding one row to the last row of raw data.
Write any text in the column you want to change to str, save, load, and then delete the last line.