pandas timestamp series to string? – Pandas

The Problem:

I am working with a pandas DataFrame that has a column of timestamps. I want to convert the timestamp column into a string vector to perform further analysis or visualizations. How can I achieve this conversion while preserving the structure and vector form of the data?

The Solutions:

Solution 1: Using the `strftime` method.

To convert a Pandas timestamp series `df[‘timestamp’]` to a string vector, you can use the following steps:

1. Use the dt accessor to access the timestamp series.
2. Use the strftime method to format the timestamp series as strings.
3. Pass a format string to strftime to specify the desired format of the output strings.

For example, consider the following Pandas dataframe df:

df = pd.DataFrame(dict(timestamp=pd.to_datetime(['2000-01-01'])))

print(df)

       timestamp
0 2000-01-01

To convert the timestamp column to a string vector, you can use the following code:

df['timestamp_string'] = df.timestamp.dt.strftime('%Y-%m-%d')

print(df)

       timestamp timestamp_string
0 2000-01-01        2000-01-01

In this example, we have used the '%Y-%m-%d' format string to format the timestamp series as strings. You can use any format string that is supported by the strftime method. See the strftime.org website for a list of supported format strings.

Solution 2: Use astype

Use astype to convert the timestamp series into a string vector:

import pandas as pd
df = pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))
df.astype(str)

This will return an array of strings:

0    2009-07-31
1    2010-01-10
2           NaT
dtype: object

This preserves the structure of the original timestamp series, so that it is still a vector.

Solution 3: Using astype()

To convert a datetime Series in-place, use the following code:

df['Column_name'] = df['Column_name'].astype(str)

This will convert each element of the ‘Column_name’ Series to a string, preserving the structure of the Series.