How to Convert Timestamp into String in Python?
I have a problem with the following code. I get an error: “strptime() argument 1 must be str, not Timestamp”.
I believe I need to convert the date from a timestamp to a string, but I am unsure how to do this.
Here’s my code:
class TweetAnalyzer:
def tweets_to_data_frame(self, ElonMuskTweets):
df = pd.DataFrame(data=[tweet.text for tweet in ElonMuskTweets], columns=['Tweets'])
df['Text length'] = np.array([len(tweet.text) for tweet in ElonMuskTweets])
df['Date and time of creation'] = np.array([tweet.created_at for tweet in ElonMuskTweets])
df['Likes'] = np.array([tweet.favorite_count for tweet in ElonMuskTweets])
df['Retweets'] = np.array([tweet.retweet_count for tweet in ElonMuskTweets])
list_of_dates = []
list_of_times = []
for date in df['Date and time of creation']:
date_time_obj = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
list_of_dates.append(date_time_obj.date())
list_of_times.append(date_time_obj.time())
df['Date'] = list_of_dates
df['Time'] = list_of_times
df['Date'] = pd.to_datetime(df['Date'])
start_date = '2018-04-13'
end_date = '2019-04-13'
mask1 = (df['Date'] >= start_date) & (df['Date'] <= end_date)
MuskTweets18_19 = df.loc[mask1]
return MuskTweets18_19.to_csv('elonmusk_tweets.csv', index=False)
I get the error in this line:
date_time_obj = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
How can I solve this problem and properly convert the timestamp to string in Python?
Using strftime() Method"
I’ve worked quite a bit on handling timestamps in Python, and if you’re dealing with a datetime object, the strftime() method is your best friend for converting it into a formatted string. Here’s how I usually do it:
from datetime import datetime
import pandas as pd
import numpy as np
class TweetAnalyzer:
def tweets_to_data_frame(self, ElonMuskTweets):
df = pd.DataFrame(data=[tweet.text for tweet in ElonMuskTweets], columns=['Tweets'])
df['Text length'] = np.array([len(tweet.text) for tweet in ElonMuskTweets])
df['Date and time of creation'] = np.array([tweet.created_at for tweet in ElonMuskTweets])
df['Likes'] = np.array([tweet.favorite_count for tweet in ElonMuskTweets])
df['Retweets'] = np.array([tweet.retweet_count for tweet in ElonMuskTweets])
list_of_dates = []
list_of_times = []
for date in df['Date and time of creation']:
date_time_obj = pd.to_datetime(date) # Ensure it's a datetime object
list_of_dates.append(date_time_obj.strftime('%Y-%m-%d'))
list_of_times.append(date_time_obj.strftime('%H:%M:%S'))
df['Date'] = list_of_dates
df['Time'] = list_of_times
df['Date'] = pd.to_datetime(df['Date'])
start_date = '2018-04-13'
end_date = '2019-04-13'
mask1 = (df['Date'] >= start_date) & (df['Date'] <= end_date)
MuskTweets18_19 = df.loc[mask1]
return MuskTweets18_19.to_csv('elonmusk_tweets.csv', index=False)
This method is great when you want full control over how the date and time are formatted!
Convert timestamp Directly Using pd.to_datetime()"
Tom’s approach is solid, especially if you need to extract separate Date and Time columns. However, if you’re looking for a cleaner way to handle the conversion and formatting all in one go, pd.to_datetime() with dt.strftime() does the trick directly. Here’s an alternative:
import pandas as pd
import numpy as np
class TweetAnalyzer:
def tweets_to_data_frame(self, ElonMuskTweets):
df = pd.DataFrame(data=[tweet.text for tweet in ElonMuskTweets], columns=['Tweets'])
df['Text length'] = np.array([len(tweet.text) for tweet in ElonMuskTweets])
df['Date and time of creation'] = np.array([tweet.created_at for tweet in ElonMuskTweets])
df['Likes'] = np.array([tweet.favorite_count for tweet in ElonMuskTweets])
df['Retweets'] = np.array([tweet.retweet_count for tweet in ElonMuskTweets])
df['Date and time of creation'] = pd.to_datetime(df['Date and time of creation']).dt.strftime('%Y-%m-%d %H:%M:%S')
start_date = '2018-04-13'
end_date = '2019-04-13'
mask1 = (df['Date'] >= start_date) & (df['Date'] <= end_date)
MuskTweets18_19 = df.loc[mask1]
return MuskTweets18_19.to_csv('elonmusk_tweets.csv', index=False)
This method simplifies the process, especially when you don’t need to break down the timestamp into separate components. It’s a neat one-liner!
Using timestamp Conversion via datetime"
I like how both Rashmi and Shilpa approached the problem. However, if you’re working with raw timestamp values, you might find it useful to convert them to datetime objects first using datetime.fromtimestamp(). It gives you fine-grained control over the process. Here’s what I do:
from datetime import datetime
import pandas as pd
import numpy as np
class TweetAnalyzer:
def tweets_to_data_frame(self, ElonMuskTweets):
df = pd.DataFrame(data=[tweet.text for tweet in ElonMuskTweets], columns=['Tweets'])
df['Text length'] = np.array([len(tweet.text) for tweet in ElonMuskTweets])
df['Date and time of creation'] = np.array([tweet.created_at for tweet in ElonMuskTweets])
df['Likes'] = np.array([tweet.favorite_count for tweet in ElonMuskTweets])
df['Retweets'] = np.array([tweet.retweet_count for tweet in ElonMuskTweets])
list_of_dates = []
list_of_times = []
for date in df['Date and time of creation']:
date_time_obj = datetime.fromtimestamp(date.timestamp()) # Convert timestamp to datetime
list_of_dates.append(date_time_obj.strftime('%Y-%m-%d'))
list_of_times.append(date_time_obj.strftime('%H:%M:%S'))
df['Date'] = list_of_dates
df['Time'] = list_of_times
df['Date'] = pd.to_datetime(df['Date'])
start_date = '2018-04-13'
end_date = '2019-04-13'
mask1 = (df['Date'] >= start_date) & (df['Date'] <= end_date)
MuskTweets18_19 = df.loc[mask1]
return MuskTweets18_19.to_csv('elonmusk_tweets.csv', index=False)
This approach works really well when you need to explicitly handle timestamps. It’s especially useful if you’re dealing with inconsistent data sources.