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Get month-day pair without a year from pandas date time

I am trying to use this, but eventually, I get the same year-month-day format where my year changed to default "1900". I want to get only month-day pairs if it is possible.
df['date'] = pd.to_datetime(df['date'], format="%m-%d")

If you transform anything to date time, you'll always have a year in it, i.e. to_datetime will always yield a date time with a year.
Without a year, you will need to store it as a string, e.g. by running the inverse of your example:
df['date'] = df['date'].dt.strftime(format="%m-%d")

Group by month in a Pandas dataframe when there is no year in the datetime object

I have a large dataset with a date_time field (object) that is in this format: 01/01 01:00:00 (month/day hour:minute:second). There is no year. I want to be able to group the dataset by month in a Pandas dataframe.
Whatever I try, I either get an error like, "Error parsing datetime string " 01/01 01:00:00" at position 3" or an out-of-bounds error. I'm a bit of a newbie here. I suspect it is a datetime formatting issue because there is no year...but I cannot figure it out.

If you don't have a year, you don't really have a date. But you can still group by month, just treat it like a string!
Something along the lines of this should work:
# create a month string column, called month_str
# the lambda function just turns the col with the yearless 'dates' into a str
# and takes only the first two characters
df['month_str'] = df['datetime'].apply(lambda x: str(x)[0:2])
df.groupby('month_str')

How to find missing dates in an excel file by python

I'm a beginner in python. I have an excel file. This file shows the rainfall amount between 2016-1-1 and 2020-6-30. It has 2 columns. The first column is date, another column is rainfall. Some dates are missed in the file (The rainfall didn't estimate). For example there isn't a row for 2016-05-05 in my file. This a sample of my excel file.
Date rainfall (mm)
1/1/2016 10
1/2/2016 5
.
.
.
12/30/2020 0
I want to find the missing dates but my code doesn't work correctly!
import pandas as pd
from datetime import datetime, timedelta
from matplotlib import dates as mpl_dates
from matplotlib.dates import date2num
df=pd.read_excel ('rainfall.xlsx')
a= pd.date_range(start = '2016-01-01', end = '2020-06-30' ).difference(df.index)
print(a)

Here' a beginner friendly way of doing it.
First you need to make sure, that the Date in your dataframe is really a date and not a string or object.
Type (or print) df.info().
The date column should show up as datetime64[ns]
If not, df['Date'] = pd.to_datetime(df['Date'], dayfirst=False)fixes that. (Use dayfirst to tell if the month is first or the day is first in your date string because Pandas doesn't know. Month first is the default, if you forget, so it would work without...)
For the tasks of finding missing days, there's many ways to solve it. Here's one.
Turn all dates into a series
all_dates = pd.Series(pd.date_range(start = '2016-01-01', end = '2020-06-30' ))
Then print all dates from that series which are not in your dataframe "Date" column. The ~ sign means "not".
print(all_dates[~all_dates.isin(df['Date'])])

Try:
df = pd.read_excel('rainfall.xlsx', usecols=[0])
a = pd.date_range(start = '2016-01-01', end = '2020-06-30').difference([l[0] for l in df.values])
print(a)
And the date in the file must like 2016/1/1

To find the missing dates from a list, you can apply Conditional Formatting function in Excel. 4. Click OK > OK, then the position of the missing dates are highlighted. Note: The last date in the date list will be highlighted.
this TRICK Is not with python,a NORMAL Trick

Resampling Pandas Timeseries so that the Date indicated the 1st of each Month

I have a Pandas timeseries where the Date indicates the last day of each month. I would like to change it so that it contains the first day of each Month. E.g., instead of '2018-08-31' to become '2018-08-01' and so on for all the Dates.
To that end I tried to resample using the 'convention' argument with the value 'start' but the method returned the timeseries intact.
For a reproducible example:
toy_data.to_json()
'{"GDP_Quarterly_Growth_Rate":{"-710294400000":-0.266691,"-707616000000":-0.266691,"-704937600000":-0.266691,"-702345600000":-0.206496,"-699667200000":-0.206496,"-697075200000":-0.206496,"-694396800000":1.564208,"-691718400000":1.564208,"-689212800000":1.564208,"-686534400000":1.504256}}'
toy_data.resample('M', convention = 'start').mean()
Returns the toy_data intact.

Change M to MS, check offset aliases:
toy_data.resample('MS', convention = 'start').mean()

Pandas: select all dates with specific month and day

I have a dataframe full of dates and I would like to select all dates where the month==12 and the day==25 and add replace the zero in the xmas column with a 1.
Anyway to do this? the second line of my code errors out.
df = DataFrame({'date':[datetime(2013,1,1).date() + timedelta(days=i) for i in range(0,365*2)], 'xmas':np.zeros(365*2)})
df[df['date'].month==12 and df['date'].day==25] = 1

Pandas Series with datetime now behaves differently. See .dt accessor.
This is how it should be done now:
df.loc[(df['date'].dt.day==25) & (cust_df['date'].dt.month==12), 'xmas'] = 1

Basically what you tried won't work as you need to use the & to compare arrays, additionally you need to use parentheses due to operator precedence. On top of this you should use loc to perform the indexing:
df.loc[(df['date'].month==12) & (df['date'].day==25), 'xmas'] = 1

An update was needed in reply to this question. As of today, there's a slight difference in how you extract months from datetime objects in a pd.Series.
So from the very start, incase you have a raw date column, first convert it to datetime objects by using a simple function:
import datetime as dt
def read_as_datetime(str_date):
# replace %Y-%m-%d with your own date format
return dt.datetime.strptime(str_date,'%Y-%m-%d')
then apply this function to your dates column and save results in a new column namely datetime:
df['datetime'] = df.dates.apply(read_as_datetime)
finally in order to extract dates by day and month, use the same piece of code that #Shayan RC explained, with this slight change; notice the dt.datetime after calling the datetime column:
df.loc[(df['datetime'].dt.datetime.month==12) &(df['datetime'].dt.datetime.day==25),'xmas'] =1