While working with data in Pandas, it is not an unusual thing to encounter time series data, and we know Pandas is a very useful tool for working with time-series data in python.
Let’s see how we can convert a dataframe column of strings (in dd/mm/yyyy format) to datetime format. We cannot perform any time series based operation on the dates if they are not in the right format. In order to be able to work with it, we are required to convert the dates into the datetime format.
Convert the column type from string to datetime format in Pandas dataframe
Convert Pandas dataframe column type from string to datetime format using pd.to_datetime() function.
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.DataFrame({'Date':['11/8/2011', '04/23/2008', '10/2/2019'], 'Event':['Music', 'Poetry', 'Theatre'], 'Cost':[10000, 5000, 15000]})# Print the dataframeprint(df)# Now we will check the data type# of the 'Date' columndf.info() |
Output:
As we can see in the output, the data type of the ‘Date’ column is object i.e. string. Now we will convert it to datetime format using pd.to_datetime() function.
Python3
# convert the 'Date' column to datetime formatdf['Date']= pd.to_datetime(df['Date'])# Check the format of 'Date' columndf.info() |
Output:
As we can see in the output, the format of the ‘Date’ column has been changed to the datetime format.
Convert Pandas dataframe column type from string to datetime format using DataFrame.astype() function
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.DataFrame({'Date':['11/8/2011', '04/23/2008', '10/2/2019'], 'Event':['Music', 'Poetry', 'Theatre'], 'Cost':[10000, 5000, 15000]})# Print the dataframeprint(df)# Now we will check the data type# of the 'Date' columndf.info() |
Output :
As we can see in the output, the data type of the ‘Date’ column is object i.e. string. Now we will convert it to datetime format using DataFrame.astype() function.
Python3
# convert the 'Date' column to datetime formatdf['Date'] = df['Date'].astype('datetime64[ns]')# Check the format of 'Date' columndf.info() |
Output :
As we can see in the output, the format of the ‘Date’ column has been changed to the datetime format.
If the data frame column is in ‘yymmdd’ format and we have to convert it to ‘yyyymmdd’ format
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data setplayer_list = [['200712',50000],['200714',51000],['200716',51500], ['200719',53000],['200721',54000], ['200724',55000],['200729',57000]]# creating a pandas dataframedf = pd.DataFrame(player_list,columns=['Dates','Patients'])# printing dataframeprint(df)print()# checking the typeprint(df.dtypes) |
Python3
# converting the string to datetime formatdf['Dates'] = pd.to_datetime(df['Dates'], format='%y%m%d')# printing dataframeprint(df)print()print(df.dtypes) |
In the above example, we change the data type of column ‘Dates’ from ‘object‘ to ‘datetime64[ns]‘ and format from ‘yymmdd’ to ‘yyyymmdd’.
Converting multiple columns from string to ‘yyyymmdd‘ format using pandas.to_datetime()
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data setplayer_list = [['20200712',50000,'20200812'], ['20200714',51000,'20200814'], ['20200716',51500,'20200816'], ['20200719',53000,'20200819'], ['20200721',54000,'20200821'], ['20200724',55000,'20200824'], ['20200729',57000,'20200824']]# creating a pandas dataframedf = pd.DataFrame( player_list,columns = ['Treatment_start', 'No.of Patients', 'Treatment_end'])# printing dataframeprint(df)print()# checking the typeprint(df.dtypes) |
Python3
# converting the string to datetime# format in multiple columnsdf['Treatment_start'] = pd.to_datetime( df['Treatment_start'], format='%Y%m%d')df['Treatment_end'] = pd.to_datetime( df['Treatment_end'], format='%Y%m%d')# printing dataframeprint(df)print()print(df.dtypes) |
In the above example, we change the data type of columns ‘Treatment_start‘ and ‘Treatment_end‘ from ‘object‘ to ‘datetime64[ns]‘ type.
