How to Load and Explore Time Series Data in Python

The Pandas library in Python provides excellent, built-in support for time series data.

Once loaded, Pandas also provides tools to explore and better understand your dataset.

In this post, you will discover how to load and explore your time series dataset.

After completing this tutorial, you will know:

  • How to load your time series dataset from a CSV file using Pandas.
  • How to peek at the loaded data and calculate summary statistics.
  • How to plot and review your time series data.

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  • Updated Apr/2019: Updated the link to dataset.
  • Update Aug/2019: Updated data loading to use new API.

Daily Female Births Dataset

In this post, we will use the Daily Female Births Dataset as an example.

This univariate time series dataset describes the number of daily female births in California in 1959.

The units are a count and there are 365 observations. The source of the dataset is credited to Newton (1988).

Below is a sample of the first 5 rows of data, including the header row.

Below is a plot of the entire dataset.

Daily Female Births Dataset

Daily Female Births Dataset

Download the dataset and place it in your current working directory with the file name “daily-total-female-births-in-cal.csv“.

Load Time Series Data

Pandas represented time series datasets as a Series.

A Series is a one-dimensional array with a time label for each row.

The series has a name, which is the column name of the data column.

You can see that each row has an associated date. This is in fact not a column, but instead a time index for value. As an index, there can be multiple values for one time, and values may be spaced evenly or unevenly across times.

The main function for loading CSV data in Pandas is the read_csv() function. We can use this to load the time series as a Series object, instead of a DataFrame, as follows:

Note the arguments to the read_csv() function.

We provide it a number of hints to ensure the data is loaded as a Series.

  • header=0: We must specify the header information at row 0.
  • parse_dates=[0]: We give the function a hint that data in the first column contains dates that need to be parsed. This argument takes a list, so we provide it a list of one element, which is the index of the first column.
  • index_col=0: We hint that the first column contains the index information for the time series.
  • squeeze=True: We hint that we only have one data column and that we are interested in a Series and not a DataFrame.

One more argument you may need to use for your own data is date_parser to specify the function to parse date-time values. In this example, the date format has been inferred, and this works in most cases. In those few cases where it does not, specify your own date parsing function and use the date_parser argument.

Running the example above prints the same output, but also confirms that the time series was indeed loaded as a Series object.

It is often easier to perform manipulations of your time series data in a DataFrame rather than a Series object.

In those situations, you can easily convert your loaded Series to a DataFrame as follows:

Further Reading

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Exploring Time Series Data

Pandas also provides tools to explore and summarize your time series data.

In this section, we’ll take a look at a few, common operations to explore and summarize your loaded time series data.

Peek at the Data

It is a good idea to take a peek at your loaded data to confirm that the types, dates, and data loaded as you intended.

You can use the head() function to peek at the first 5 records or specify the first n number of records to review.

For example, you can print the first 10 rows of data as follows.

Running the example prints the following:

You can also use the tail() function to get the last n records of the dataset.

Number of Observations

Another quick check to perform on your data is the number of loaded observations.

This can help flush out issues with column headers not being handled as intended, and to get an idea on how to effectively divide up data later for use with supervised learning algorithms.

You can get the dimensionality of your Series using the size parameter.

Running this example we can see that as we would expect, there are 365 observations, one for each day of the year in 1959.

Querying By Time

You can slice, dice, and query your series using the time index.

For example, you can access all observations in January as follows:

Running this displays the 31 observations for the month of January in 1959.

This type of index-based querying can help to prepare summary statistics and plots while exploring the dataset.

Descriptive Statistics

Calculating descriptive statistics on your time series can help get an idea of the distribution and spread of values.

This may help with ideas of data scaling and even data cleaning that you can perform later as part of preparing your dataset for modeling.

The describe() function creates a 7 number summary of the loaded time series including mean, standard deviation, median, minimum, and maximum of the observations.

Running this example prints a summary of the birth rate dataset.

Plotting Time Series

Plotting time series data, especially univariate time series, is an important part of exploring your data.

This functionality is provided on the loaded Series by calling the plot() function.

Below is an example of plotting the entire loaded time series dataset.

Running the example creates a time series plot with the number of daily births on the y-axis and time in days along the x-axis.

Daily Total Female Births Plot

Daily Total Female Births Plot

Further Reading

If you’re interested in learning more about Pandas’ functionality working with time series data, see some of the links below.


In this post, you discovered how to load and handle time series data using the Pandas Python library.

Specifically, you learned:

  • How to load your time series data as a Pandas Series.
  • How to peek at and calculate summary statistics of your time series data.
  • How to plot your time series data.

Do you have any questions about handling time series data in Python, or about this post?
Ask your questions in the comments below and I will do my best to answer.

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41 Responses to How to Load and Explore Time Series Data in Python

  1. Avatar
    Vishal December 9, 2016 at 9:10 am #

    You may find this useful:

  2. Avatar
    Hans April 22, 2017 at 1:54 pm #

    How can we extract a X and a Y from the series object, in regard to this tutorial?:

  3. Avatar
    wong June 5, 2017 at 3:30 pm #

    when I use print(series[‘1959-01’]), it gave me key error.

    • Avatar
      Jason Brownlee June 6, 2017 at 9:22 am #

      Sorry, I have not seen this.

      Confirm you have the correct data, perhaps open the CSV file in a text editor and confirm you have no footer.

      Consider printing all of the loaded data to see that it is indeed loaded as expected.

      Let me know how you go.

    • Avatar
      X July 3, 2017 at 4:29 pm #

      I got the same problem, looks like it’s not parsed as Date format, but I have no idea how to convert it.

      • Avatar
        X July 3, 2017 at 4:38 pm #

        I figured it out. The problem was due to that I forgot to remove the footer information of the csv file.

        • Avatar
          Jason Brownlee July 6, 2017 at 9:58 am #

          Glad to hear it.

        • Avatar
          Violeta July 31, 2017 at 8:10 pm #

          Hi, removing the tail by hand is not a good option if you had loads of files like this. Is there a way to handle this automatically with pandas?

  4. Avatar
    Firnas July 2, 2017 at 5:45 pm #

    Error when run the first snippet of code 🙁

    Traceback (most recent call last):
    File “/usr/lib/python2.7/”, line 68, in
    import os
    File “/usr/lib/python2.7/”, line 400, in
    import UserDict
    File “/usr/lib/python2.7/”, line 116, in
    import _abcoll
    File “/usr/lib/python2.7/”, line 11, in
    from abc import ABCMeta, abstractmethod

  5. Avatar
    Mohammed Helal December 5, 2017 at 9:32 am #

    I’m getting an error when using pyplot(series):

    ValueError: could not convert string to float: ‘1959-12-31’

    I found a way to change to datetime object then date2num to make it work, but I was curious why passing in the data as you did didn’t work for me? I’m using Python 3.

  6. Avatar
    Prasad May 2, 2018 at 8:33 pm #

    I am getting the below error while.

    FileNotFoundError: File b’daily-total-female-births-in-cal.csv’ does not exist

    • Avatar
      Jason Brownlee May 3, 2018 at 6:32 am #

      You need to download the dataset and place it in the same directory as your code.

  7. Avatar
    suchita kadam June 19, 2018 at 5:06 pm #

    i am using python 3. so for plotting time series, i have to change index to date time index. otherwise “value error” exception occured. error occurred because index values are in string format and for plotting index values should be other than string. in our case, it should be in datetime format.

    import matplotlib.pyplot as plt

    or you can do


    • Avatar
      Jason Brownlee June 20, 2018 at 6:22 am #


    • Avatar
      supradha April 9, 2019 at 12:38 am #

      Thanks. this saved from huge confusion

  8. Avatar
    Trung July 13, 2018 at 7:28 pm #

    I wrote code as below:

    from pandas import read_csv
    from matplotlib import pyplot

    from sklearn.metrics import mean_squared_error
    from math import sqrt

    series = read_csv(‘daily_births.csv’, header=0, index_col=0, parse_dates=True, squeeze=True)
    #series = read_csv(‘daily_births.csv’, header=0, index_col=0)
    predictions = []
    actual = series.values[1:]
    rmse = sqrt(mean_squared_error(actual, predictions))

    There was an error: ValueError: Found input variables with inconsistent numbers of samples: [364, 0]. Please help

  9. Avatar
    Richard Dunn February 9, 2019 at 10:17 am #

    Hi Jason, how might you set up time series data that is sparse and grouped.
    Would any of these ideas work:
    (1) Create averages as per the largest time span so as to eliminate any gaps
    (2) Smooth out the gaps some how
    Sorry to carry on but if you were trying to model ts data in an xgboost model and needed to predict the latest ts period how could you deal with a distinct lack of data in this period, would xgboost automatically handle this for us or would you advise on more traditional ts methods.

    • Avatar
      Jason Brownlee February 10, 2019 at 9:38 am #

      I would recommend brainstorming as many different framings of the problem as possible and test each to see what worked.

  10. Avatar
    Berns B. August 7, 2019 at 9:29 am #

    Went back to this tutorial why is it doing like this:
    AttributeError Traceback (most recent call last)
    —-> 1 series=Series.from_csv(‘daily-total-female-births.csv’,headers=0)

    AttributeError: type object ‘Series’ has no attribute ‘from_csv’

    • Avatar
      Jason Brownlee August 7, 2019 at 2:19 pm #

      The error suggest that perhaps your version of Pandas is not up to date?

  11. Avatar
    Berns B. August 7, 2019 at 9:49 am #

    Tried visiting the website for Pandas Series but its down? Had to change this to:

    series = pd.read_csv(‘daily-total-female-births.csv’, header=0)

    now that works!

  12. Avatar
    Berns B. August 7, 2019 at 3:11 pm #

    This works well if you are on Python 2.7 then you will not run into trouble using the Series.from_csv(‘filename.csv’) or printing the statistical results by print(series.describe())

    Be sure though that for matplotlib for an older version in Python 2.7 execute this early before the pyplot.plot():

    from pandas.plotting import register_matplotlib_converters

  13. Avatar
    Berns B. August 7, 2019 at 4:27 pm #

    Also some bit of modification from series to dataframe conversion here:
    import pandas as pd
    dataframe =pd.DataFrame(series)

  14. Avatar
    Dominique April 22, 2020 at 3:38 pm #

    Hi Jason,

    With the following piece of code:
    # Load birth data using read_csv
    from pandas import read_csv
    series = read_csv(‘daily-total-female-births-in-cal.csv’, header=0, parse_dates=[0], index_col=0, squeeze=True)

    The returned type is a data frame and not a series. Do you have a suggestion?

    And then I get a keyError ‘1959-01’ when I try to print the series. Any idea?

    Thanks for help,
    Kind regards,


  15. Avatar
    Dominique April 22, 2020 at 8:55 pm #

    Hi Jason,

    during lunch I corrected the code. My understanding is that your code is for Python 2.7. As I am running Python 3.7, it might explain why it is not working.

    Below is the piece of code that works for me in Python 3.7.

    Anyway thank you very much for your always helpful and well guided post.

    Kind regards
    # Python version
    import sys
    print(‘Python: {}’.format(sys.version))

    # Load birth data using read_csv
    import pandas as pd
    sr = pd.read_csv(‘daily-total-female-births.csv’, delimiter=’;’)

    # Print the first few rows using the head() function.
    print(f’\nThe type is: {type(sr)}’)


    # Print the dimensions of the dataset using the size attribute.
    print(f’\nSize is:{sr.Date.size}’)

    # Query the dataset using a date-time string.
    print(f’\nAll dates before March:’)
    print(sr[sr.Date <= '1959-01-03'])

    # Print summary statistics of the observations.

    # Data Visualization
    from matplotlib import pyplot


    • Avatar
      Jason Brownlee April 23, 2020 at 6:04 am #

      The code as-is in the tutorials works with python 2 and 3 without change.

      I’m happy to hear you fixed your problem.

  16. Avatar
    John September 8, 2020 at 1:07 pm #

    Let’s say I read in some data like your example:
    1959-01-04 31
    1959-01-05 44
    1959-01-06 29
    or perhaps comma-separated values with hours, minutes and seconds as well.
    But I have a function that accepts time only in the form of seconds since the 1970 epoch.
    Is there a general method for converting the datetime column to seconds? I know how to convert a single value, but I want to convert thousands of values at once, and preferably not in a loop.

    • Avatar
      Jason Brownlee September 8, 2020 at 1:39 pm #

      Yes, I believe Python will represent datetimes internally as time from epoch and provide a function to retrieve the epoch time directly. I recommend checking the python or pandas API documentation for handling date-times.

  17. Avatar
    John September 9, 2020 at 8:28 am #

    I have actually spent hours with the Python and pandas documentation, and on browsing various forums, trying to work out how to do it. I was expecting it to be a trivial exercise, and probably the answer will turn out to be quite simple. The problem is, I come from a Matlab background, and am accustomed to working with arrays.. I have found about a dozen ways of converting to “seconds from epoch” for a single datetime value, but none of them accept more than a single input time. But, thanks anyway.

    • Avatar
      Jason Brownlee September 9, 2020 at 1:31 pm #

      Sorry to hear that John. I’m eager to help, but I don’t know the API call off the top of my head.

      Perhaps try posting your question to

      • Avatar
        John September 9, 2020 at 3:45 pm #

        Good idea, I will do that. Thanks

  18. Avatar
    James T February 27, 2021 at 4:02 pm #

    This is a deploymeny for LSTM model in flask python, the first column in the csv file I uploaded for prediction is the datetime, how to remain again the first column datatime after i have made prediction and download as a new csv file, Please share me some tips, its really stuck me for a while

    from flask import Flask, make_response, request, render_template
    import io
    from io import StringIO
    import csv
    import pandas as pd
    import numpy as np
    import pickle
    import os
    from keras.models import load_model
    from sklearn.preprocessing import MinMaxScaler
    from statsmodels.tsa.arima_model import ARIMAResults

    app = Flask(__name__)

    def form():
    return “””

    Let’s TRY to Predict..

    Insert your CSV file and then download the Result


    @app.route(‘/transform’, methods=[“POST”])
    def transform_view():
    if request.method == ‘POST’:
    f = request.files[‘data_file’]
    if not f:
    return “No file”

    stream = io.StringIO(“UTF8”), newline=None)
    csv_input = csv.reader(stream)
    #print(“file contents: “, file_contents)
    for row in csv_input:
    result =
    df = pd.read_csv(StringIO(result), usecols=[1])

    # load the model from disk
    model = load_model(‘model.h5’)
    dataset = df.values
    dataset = dataset.astype(‘float32′)
    scaler = MinMaxScaler(feature_range=(0, 1))
    dataset = scaler.fit_transform(dataset)
    dataset = np.reshape(dataset, (dataset.shape[0], 1, dataset.shape[1]))
    df = model.predict(dataset)
    transform = scaler.inverse_transform(df)
    df_predict = pd.DataFrame(transform, columns=[“predicted value”])

    response = make_response(df_predict.to_csv(index = True , encoding=’utf8’))
    response.headers[“Content-Disposition”] = “attachment; filename=result.csv”
    return response

    if __name__ == “__main__”:, port = 9000, host = “localhost”)

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