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I want to add a list of minutes to datetime64 columns into a new df column.
I tried using datetime.timedelta(minutes=x) in a for loop. But as a result, it is adding a constant value to all of my rows. How do I resolve this?
for x in wait_min:
data['New_datetime'] = data['Date'] + datetime.timedelta(minutes=x)
I expect to iterate through the list and add corresponding minutes, but this is adding a constant value of 16 minutes to each row.
Let us try
data['Date'] + pd.to_timedelta(wait_min, unit='m')
pandas sums two Series element-wise, if they have the same length. All you need to do is create a Series of timedelta objects.
So if wait_min is a list of minutes of length equal to the number of rows in your dataframe, this will do:
data['New_datetime'] = data['Date'] + pd.Series([datetime.timedelta(minutes=x) for x in wait_min])
The following changes worked for me:
for i, x in enumerate(wait_min):
data['New_Datetime'].iloc[i] = data['Date'].iloc[i] + datetime.timedelta(minutes=x)
might not be the best solution, but this works for what I was trying to do.
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I am working on a dataframe. Data in the image
Q. I want the number of shows released per year but if I'm applying count() function, it's giving me 6 instead of 3. Could anyone suggest how do I get the correct value count.
To get unique value of single year, you can use
count = len(df.loc[df['release_year'] == 1945, 'show_id'].unique())
# or
count = df.loc[df['release_year'] == 1945, 'show_id'].nunique()
To summarize unique value of dataframe by year, you can drop_duplicates() on column show_id first.
df.drop_duplicates(subset=['show_id']).groupby('release_year').count()
Or use value_counts() on column after dropping duplicates.
df.drop_duplicates(subset=['show_id'])['release_year'].value_counts()
df['show_id'].nunique().count()
should do the job.
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i want a range of values from two columns From and To since the number in To column should be included in range of values so i'm adding 1 to that as shown in below
df.apply(lambda x : range(x['From'],x['To']+1),1)
df.apply(lambda x : ','.join(map(str, range(x['From'],x['To']))),1)
i need output some thing like this
if from value starts from 5 and To value ends with 11
myoutput should be like this
5,6,7,8,9,10,11
i'm getting till 10 only even i have added +1 to range of end value
df:
----
From To
15887 16251
15888 16252
15889 16253
15890 16254
and range should be written in new column
Try this:
df=pd.DataFrame({'From':[15887,15888,15889,15890],'To':[16251,16252,16253,16254]})
df['Range']=[list(range(i,k+1)) for i,k in zip(df['From'],df['To'])]
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df_main['month_1']= np.where(df_main['month_1'] >='2020-02-01',0,df_main['month_1'])
I need all the items in the month_1 column to be zero if the date is February 1st, 2020.
I tried '02/01/2020' format as well, which doesn't work.
since your column datatype is timestamp you can not use str '2020-02-01' to compare with your column so you need also a timestamp value: pd.Timestamp(2020, 2,1)
you can use pandas.Series.map:
df_main['month_1'] = df_main['month_1'].map(lambda x: 0 if x >= pd.Timestamp(2020, 2,1) else x)
or you can filter and assign:
df_main['month_1'][ df_main['month_1'] >= pd.Timestamp(2020, 2,1)] = 0
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I have the user input two lists, one for sizes one for minutes they are each stored in a list. For example they can input sizes: 111, 121 and for minutes, 5, 10, 15.
I want to have the dataframe have columns that are named by the size and minute. (I did a for loop to extract each size and minute.) For example I want the columns to say 111,5 ; 111,10; 111;15, etc. I tried to do df[size+minute]=values (Values is data I want to input into each column) but instead the column name is just the values added up so I got the column name to be 116 instead of 111,5.
If you have two lists:
l = [111,121]
l2 = [5,10,15]
Then you can use list comprehension to form your column names:
col_names = [str(x)+';'+str(y) for x in l for y in l2]
print(col_names)
['111;5', '111;10', '111;15', '121;5', '121;10', '121;15']
And create a dataframe with these column names using pandas.DataFrame:
df = pd.DataFrame(columns=col_names)
If we add a row of data:
row = pd.DataFrame([[1,2,3,4,5,6]])
row.columns = col_names
df = df.append(pd.DataFrame(row))
We can see that our dataframe looks like this:
print(df)
111;5 111;10 111;15 121;5 121;10 121;15
0 1 2 3 4 5 6
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I have a Panda and want to do a calculation based on an existing column.
However, the apply. function is not working for some reason.
It's something like letssay
df = pd.DataFrame({'Age': age, 'Input': input})
and the input column is something like [1.10001, 1.49999, 1.60001]
Now I want to add a new column to the Dataframe, that is doing the following:
Add 0.0001 to each element in column
Multiply each value by 10
Transform each value of new column to int
Use Series.add, Series.mul and Series.astype:
#input is python code word (builtin), so better dont use it like variable
inp = [1.10001, 1.49999, 1.60001]
age = [10,20,30]
df = pd.DataFrame({'Age': age, 'Input': inp})
df['new'] = df['Input'].add(0.0001).mul(10).astype(int)
print (df)
Age Input new
0 10 1.10001 11
1 20 1.49999 15
2 30 1.60001 16
You could make a simple function and then apply it by row.
def f(row):
return int((row['input']+0.0001)*10))
df['new'] = df.apply(f, axis=1)