I have tried multiple ways to achieve this for ex:
inputpd = pd.DataFrame(inputpd.columns,dtype=str)
But it does not work. sorry for asking this question as i am beginner to spark.
If it's a Pandas DataFrame:
df = df.astype(str)
The easiest way I think it is:
df = df.applymap(str)
df is your dataframe.
Related
I want to get rid of the hours and minutes in the pandas dataframe and convert them to days. The value type in the data is datetime.datetime but when I use the .dt.date function it gives an error.here is the code
df = pd.DataFrame({'id': ['45259191000','45488870311'], 'time': ['2022-10-04 08:57:00', '2022-10-07 11:17:00']})
print(type(df.iat[0, 0]))
df['new'] = df['time'].dt.date
display(df)
this code returns Can only use .dt accessor with datetimelike value
and my datatype <class 'datetime.datetime'> thank you in advance. i hope answer is not very obvious.
try to convert the 'time' column to pandas datetime before you work with it:
df['time'] = pd.to_datetime(df['time'])
Sounds basic, but...
I have a dataframe df with (yy, mm, dd, value1, value2,...)
df1 = df.groupby(['yy','dd'], as_index = False).agg({'value1':['count'],'value2':['sum']})
working ok, returning a df1 multi index object, that I can 'visualize' e.g. df1.info()
Q: how to convert this df1 into a 'basic' 2D DataFrame.
You need to drop the multilevel from the pandas column, and then reset index. You can try this:-
df.groupby(['yy','dd'], as_index = True).agg({'value1':['count'],'value2':['sum']})
df1.columns = df1.columns.droplevel()
df1.reset_index(inplace=True)
Hope this solves your problem.
Suppose that you have a pandas DataFrame named df with columns ['a','b','c','d','e'] and you want to create a new DataFrame newdf with columns 'b' and 'd'. There are two possible ways to do this:
newdf = df[['b','d']]
or
newdf = df.loc[:,['b','d']]
The first is using the indexing operator. The second is using .loc. Is there a reason to prefer one over the other?
Thanks to #coldspeed, it seems that newdf = df.loc[:,['b','d']] is preferred to avoid the dreaded SettingWithCopyWarning.
So I learned from this answer to this question that in pandas 0.20.3 and above reassigning the values of a dataframe works without giving the SettingWithCopyWarning in many ways as follows:
df = pd.DataFrame(np.ones((5,6)),columns=['one','two','three',
'four','five','six'])
df.one *=5
df.two = df.two*5
df.three = df.three.multiply(5)
df['four'] = df['four']*5
df.loc[:, 'five'] *=5
df.iloc[:, 5] = df.iloc[:, 5]*5
HOWEVER
If I were to take a part of that dataframe like this for example:
df1 = df[(df.index>1)&(df.index<5)]
And then try one of the above methods for reassigning a column like so:
df.one *=5
THEN I will get the SettingWithCopyWarning.
So is this a bug or am I just missing something about the way pandas expects for this kind of operation to work?
Let us consider a pandas DataFrame (df) like the one shown above.
How do I convert it to a pandas Series?
Just select the single column of your frame
df['Count']
result = pd.Series(df['Count'])