当我们在 Python 的 Pandas DataFrame 中处理数据时,经常会遇到时间序列数据。Panday 是一个强大的工具,可以在 Python 中处理时间序列数据,我们可能需要在给定的数据集中将字符串转换为 Datetime 格式。
在本教程中,我们将学习如何将字符串的 DataFrame 列转换为日期时间格式“dd/mm/yy”。如果日期不是所需的格式,用户就不能对其执行任何基于时间序列的操作。为了解决这个问题,我们需要将日期转换成所需的日期时间格式。
Python 中转换数据类型格式的不同方法:
在本节中,我们将讨论将 Pandas DataFrame 列的数据类型从字符串更改为日期时间的不同方法:
方法 1:使用 pandas.to_datetime()函数
在这种方法中,我们将使用“pandas.to_datetime()”函数来转换 Pandas DataFrame 列中的数据类型。
示例:
import pandas as pnd
# Creating the dataframe
data_frame = pnd.DataFrame({'Date':['12/05/2021', '11/21/2018', '01/12/2020'],
'Event':['Music- Dance', 'Poetry- Songs', 'Theatre- Drama'],
'Cost':[15400, 7000, 25000]})
# Print the dataframe
print ("The data is: ")
print (data_frame)
# Here, we are checking the data type of the 'Date' column
data_frame.info()
输出:
The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null object
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: int64(1), object(2)
memory usage: 200.0+ bytes
在这里,在输出中,我们可以看到数据帧中“日期”列的数据类型是“对象”,这意味着它是一个字符串。现在,我们将使用“pnd.to_datetime()”函数将数据类型转换为日期时间格式:
import pandas as pnd
# Creating the dataframe
data_frame = pnd.DataFrame({'Date':['12/05/2021', '11/21/2018', '01/12/2020'],
'Event':['Music- Dance', 'Poetry- Songs', 'Theatre- Drama'],
'Cost':[15400, 7000, 25000]})
# Print the dataframe
print ("The data is: ")
print (data_frame)
# For converting the 'Date' column of DataFrame into datetime format
data_frame['Date'] = pnd.to_datetime(data_frame['Date'])
# Here, we are checking the data type of the 'Date' column
data_frame.info()
输出:
The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null datetime64[ns]
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 200.0+ bytes
现在,我们可以看到数据帧中“数据”列的格式已经更改为日期时间格式。
方法 2:使用 DataFrame.astype()函数。
在这种方法中,我们将使用“DataFrame.astype()”函数来转换 Pandas DataFrame 列中的数据类型。
示例:
import pandas as pnd
# Creating the dataframe
data_frame = pnd.DataFrame({'Date':['12/05/2021', '11/21/2018', '01/12/2020'],
'Event':['Music- Dance', 'Poetry- Songs', 'Theatre- Drama'],
'Cost':[15400, 7000, 25000]})
# Print the dataframe
print ("The data is: ")
print (data_frame)
# Here, we are checking the data type of the 'Date' column
data_frame.info()
输出:
The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null object
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: int64(1), object(2)
memory usage: 200.0+ bytes
在这里,在输出中,我们可以看到数据帧中“日期”列的数据类型是“对象”,这意味着它是一个字符串。现在,我们将使用“数据帧类型()”函数将数据类型转换为日期时间格式:
import pandas as pnd
# Creating the dataframe
data_frame = pnd.DataFrame({'Date':['12/05/2021', '11/21/2018', '01/12/2020'],
'Event':['Music- Dance', 'Poetry- Songs', 'Theatre- Drama'],
'Cost':[15400, 7000, 25000]})
# Print the dataframe
print ("The data is: ")
print (data_frame)
# For converting the 'Date' column of DataFrame into datetime format
data_frame['Date'] = data_frame['Date'].astype('datetime64[ns]')
# Here, we are checking the data type of the 'Date' column
data_frame.info()
输出:
The data is:
Date Event Cost
0 12/05/2021 Music- Dance 15400
1 11/21/2018 Poetry- Songs 7000
2 01/12/2020 Theatre- Drama 25000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 3 non-null datetime64[ns]
1 Event 3 non-null object
2 Cost 3 non-null int64
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 200.0+ bytes
现在,我们可以看到,通过使用 data_frame['Date'],DataFrame 中“Data”列的格式已更改为 datetime 格式。astype('datetime64[ns])。
方法 3:
假设我们在 DataFrame 列中有一个“yymmdd”格式的日期,我们必须将其从字符串转换为日期时间格式。
示例:
import pandas as pnd
# Now, we will initialize the nested list with Dataset
play_list = [['210302', 67000], ['210901', 62000], ['210706', 61900],
['210402', 59000], ['210802', 74000],
['210804', 54050], ['210109', 57650], ['210509', 67300], ['210209', 76600]]
# Creating a pandas DataFrame
data_frame = pnd.DataFrame(play_list,columns = ['Date','Patient Number'])
# Print the dataframe
print ("The data is: ")
print (data_frame)
# Here, we are checking the data type of the 'Date' column
print (data_frame.dtypes)
输出:
The data is:
Date Patient Number
0 210302 67000
1 210901 62000
2 210706 61900
3 210402 59000
4 210802 74000
5 210804 54050
6 210109 57650
7 210509 67300
8 210209 76600
Date object
Patient Number int64
dtype: object
这里,在输出中,我们可以看到数据帧中“日期”列的数据类型是“对象”,也就是说,它是字符串。现在,我们将使用“data frame[' Date ']= PND . to datetime(data _ frame[' Date '],format = '%y%m%d ')”函数将数据类型转换为 datetime 格式。
import pandas as pnd
# Now, we will initialize the nested list with Dataset
play_list = [['210302', 67000], ['210901', 62000], ['210706', 61900],
['210402', 59000], ['210802', 74000],
['210804', 54050], ['210109', 57650], ['210509', 67300], ['210209', 76600]]
# creating a pandas dataframe
data_frame = pnd.DataFrame(play_list,columns = ['Date','Patient Number'])
# Print the dataframe
print ("The data is: ")
print (data_frame)
# For converting the 'Date' column of DataFrame into datetime format
data_frame['Date'] = pnd.to_datetime(data_frame['Date'], format = '%y%m%d')
# Here, we are checking the data type of the 'Date' column
print (data_frame.dtypes)
输出:
The data is:
Date Patient Number
0 210302 67000
1 210901 62000
2 210706 61900
3 210402 59000
4 210802 74000
5 210804 54050
6 210109 57650
7 210509 67300
8 210209 76600
Date datetime64[ns]
Patient Number int64
dtype: object
在上面的代码中,我们通过使用“PND . to datetime(data frame[' Date '],format = '%y%m%d ')”函数,将“Date”列的数据类型从“object”更改为“datetime64[ns]”。
方法 4:
我们可以使用“pandas.to_datetime()”函数将多列从“string”转换为“datetime”格式,这意味着“YYYYMMDD”格式。
# Initializing the nested list with Data set
Dataset_list = [['20210612', 54000, '20210812'],
['20210814', 65000, '20210614'],
['20210316', 71500, '20210316'],
['20210519', 45000, '20210119'],
['20210221', 98000, '20210221'],
['20210124', 23000, '20210724'],
['20210929', 12000, '20210924']]
# creating a pandas dataframe
data_frame = pnd.DataFrame(
Dataset_list, columns = ['Treatment_starting_Date',
'Patients Number',
'Treatment_ending_Date'])
# Print the dataframe
print ("The data is: ")
print (data_frame)
# Here, we are checking the data type of the 'Date' column
print (data_frame.dtypes)
输出:
The data is:
Treatment_starting_Date Patients Number Treatment_ending_Date
0 20210612 54000 20210812
1 20210814 65000 20210614
2 20210316 71500 20210316
3 20210519 45000 20210119
4 20210221 98000 20210221
5 20210124 23000 20210724
6 20210929 12000 20210924
Treatment_starting_Date object
Patients Number int64
Treatment_ending_Date object
dtype: object
在这里,在输出中,我们可以看到数据帧中“日期”列的数据类型是“对象”,这意味着它是一个字符串。现在,我们将使用“pnd.to_datetime(data_frame[''],format = '%y%m%d ')函数将数据类型“Date”列转换为 datetime 格式。
import pandas as pnd
# Initializing the nested list with Data set
Dataset_list = [['20210612', 54000, '20210812'],
['20210814', 65000, '20210614'],
['20210316', 71500, '20210316'],
['20210519', 45000, '20210119'],
['20210221', 98000, '20210221'],
['20210124', 23000, '20210724'],
['20210929', 12000, '20210924']]
# creating a pandas dataframe
data_frame = pnd.DataFrame(
Dataset_list, columns = ['Treatment_starting_Date',
'Patients Number',
'Treatment_ending_Date'])
# Print the dataframe
print ("The data is: ")
print (data_frame)
# For converting the multiple columns of DataFrame into datetime format
data_frame['Treatment_starting_Date'] = pnd.to_datetime(
data_frame['Treatment_starting_Date'],
format = '%Y%m%d'
)
data_frame['Treatment_ending_Date'] = pnd.to_datetime(
data_frame['Treatment_ending_Date'],
format = '%Y%m%d'
)
# Here, we are checking the data type of the 'Date' column
print (data_frame.dtypes)
输出:
The data is:
Treatment_starting_Date Patients Number Treatment_ending_Date
0 20210612 54000 20210812
1 20210814 65000 20210614
2 20210316 71500 20210316
3 20210519 45000 20210119
4 20210221 98000 20210221
5 20210124 23000 20210724
6 20210929 12000 20210924
Treatment_starting_Date datetime64[ns]
Patients Number int64
Treatment_ending_Date datetime64[ns]
dtype: object
在上面的输出中,我们可以看到“治疗开始日期”和“治疗结束日期”的数据类型已经通过使用“pnd.to_datetime()”函数更改为日期时间格式。
结论
在本教程中,我们学习了在 Python 中将 Pandas DataFrame 的列类型从字符串转换为日期时间的不同方法。