How to Process a DataFrame With Millions of Rows in Seconds

Roman Orac
Aug 21, 2021

Contents Outline

How to Process a DataFrame With Millions of Rows in Seconds

Aug 21, 2021 6 minutes read

Yet another Python library for Data Analysis that You Should Know About — and no, I am not talking about Spark or Dask

Big Data Analysis in Python is having its renaissance. It all started with NumPy, which is also one of the building blocks behind the tool I am presenting in this article.

In 2006, Big Data was a topic that was slowly gaining traction, especially with the release of Hadoop. Pandas followed soon after with its DataFrames. 2014 was the year when Big Data became mainstream, also Apache Spark was released that year. In 2018 came Dask and other libraries for data analytics in Python.

Each month I find a new Data Analytics tool, which I am eager to learn. It is a worthy investment of spending an hour or two on tutorials as it can save you a lot of time in the long run. It’s also important to keep in touch with the latest tech.

While you might expect that this article will be about Dask you are wrong. I found another Python library for data analysis that you should know about.

Like Python, it is equally important that you become proficient in SQL. In case you aren’t familiar with it, and you have some money to spare, check out this course: Master SQL, the core language for Big Data analysis.

Big Data Analysis in Python is having its renaissance

In case you’re interested, Udacity offers Free Access to:

- Intro to Machine Learning with PyTorch
- Deep Learning Nanodegree and more


Meet Vaex


Vaex is a high-performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.

It can calculate basic statistics for more than a billion rows per second. It supports multiple visualizations allowing interactive exploration of big data.

What’s the difference between Vaex and Dask?


Vaex is not similar to Dask but is similar to Dask DataFrames, which are built on top pandas DataFrames. This means that Dask inherits pandas issues, like high memory usage. This is not the case Vaex.

Vaex doesn’t make DataFrame copies so it can process bigger DataFrame on machines with less main memory.

Both Vaex and Dask use lazy processing. The only difference is that Vaex calculates the field when needed, wherewith Dask we need to explicitly use the compute function.

Data needs to be in HDF5 or Apache Arrow format to take full advantage of Vaex.

How to install Vaex?

To install Vaex is as simple as installing any other Python package:
pip install vaex

Let’s take Vaex to a test drive



Let’s create a pandas DataFrame with 1 million rows and 1000 columns to create a big data file.
import vaex
import pandas as pd
import numpy as np
n_rows = 1000000
n_cols = 1000
df = pd.DataFrame(np.random.randint(0, 100, size=(n_rows, n_cols)), columns=['col%d' % i for i in range(n_cols)])
df.head()


First few lines in a Pandas Dataframe (image made by author)

How much main memory does this DataFrame use?
df.info(memory_usage='deep')


Let’s save it to disk so that we can read it later with Vaex.
file_path = 'big_file.csv'
df.to_csv(file_path, index=False)

We wouldn’t gain much by reading the whole CSV directly with Vaex as the speed would be similar to pandas. Both need approximately 85 seconds on my laptop.

We need to convert the CSV to HDF5 (the Hierarchical Data Format version 5) to see the benefit with Vaex. Vaex has a function for conversion, which even supports files bigger than the main memory by converting smaller chunks.

If you cannot open a big file with pandas, because of memory constraints, you can covert it to HDF5 and process it with Vaex.
dv = vaex.from_csv(file_path, convert=True, chunk_size=5_000_000)

This function creates an HDF5 file and persists it to disk.

What’s the datatype of dv?
type(dv)
# output
vaex.hdf5.dataset.Hdf5MemoryMapped

Now, let’s read the 7.5 GB dataset with Vaex — We wouldn’t need to read it again as we already have it in dv variable. This is just to test the speed.
dv = vaex.open('big_file.csv.hdf5')

Vaex needed less than 1 second to execute the command above. But Vaex didn’t actually read the file, because of lazy loading, right?

Let’s force to read it by calculating a sum of col1.
suma = dv.col1.sum()
suma
# Output
# array(49486599)

I was really surprised by this one. Vaex needed less than 1 second to calculate the sum. How is that possible?

Opening such data is instantaneous regardless of the file size on disk. Vaex will just memory-map the data instead of reading it in memory. This is the optimal way of working with large datasets that are larger than available RAM.

Plotting

Vaex is also fast when plotting data. It has special plotting functions plot1d, plot2d and plot2d_contour.
dv.plot1d(dv.col2, figsize=(14, 7))


Plotting with Vaex (image made by author)

Virtual columns

Vaex creates a virtual column when adding a new column, — a column that doesn’t take the main memory as it is computed on the fly.
dv['col1_plus_col2'] = dv.col1 + dv.col2
dv['col1_plus_col2']


The virtual column in Vaex (image made by author)

Efficient filtering

Vaex won’t create DataFrame copies when filtering data, which is much more memory efficient.
dvv = dv[dv.col1 > 90]

Aggregations


Aggregations work slightly differently than in pandas, but more importantly, they are blazingly fast.

Let’s add a binary virtual column where col1 ≥ 50.
dv['col1_50'] = dv.col1 >= 50

Vaex combines group by and aggregation in a single command. The command below groups data by the “col1_50” column and calculates the sum of the col3 column.
dv_group = dv.groupby(dv['col1_50'], agg=vaex.agg.sum(dv['col3']))
dv_group


Aggregations in Vaex (image made by author)

Joins

Vaex joins data without making memory copies, which saves the main memory. Pandas users will be familiar with the join function:
dv_join = dv.join(dv_group, on=’col1_50')

Conclusion


In the end, you might ask: Should we simply switch from pandas to Vaex? The answer is a big NO.

Pandas is still the best tool for data analysis in Python. It has well-supported functions for the most common data analysis tasks.

When it comes to bigger files, pandas might not be the fastest tool. This is a great place for Vaex to jump in.

Vaex is a tool you should add to your Data Analytics toolbox.

When working on an analysis task where pandas is too slow or simply crashes, pull Vaex out of your toolbox, filter out the most important entries and continue the analysis with pandas.

Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.

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In this article, we’re going to explore some lesser-known but very useful pandas methods for manipulating Series objects. Some of these methods are related only to Series, the others — both to Series and DataFrames, having, however, specific features when used with both structure types.1. is_uniqueAs its name sugests, this method checks if all the values of a Series are unique:import pandas as pd print(pd.Series([1, 2, 3, 4]).is_unique) print(pd.Series([1, 2, 3, 1]).is_unique) Output: True False 2 & 3. is_monotonic and is_monotonic_decreasingWith these 2 methods, we can check if the values of a Series are in ascending/descending order:print(pd.Series([1, 2, 3, 8]).is_monotonic) print(pd.Series([1, 2, 3, 1]).is_monotonic) print(pd.Series([9, 8, 4, 0]).is_monotonic_decreasing) Output: True False TrueBoth methods work also for a Series with string values. In this case, Python uses a lexicographical ordering under the hood, comparing two subsequent strings character by character. It’s not the same as just an alphabetical ordering, and actually, the example with the numeric data above is a particular case of such an ordering. As the Python documentation says,Lexicographical ordering for strings uses the Unicode code point number to order individual characters.In practice, it mainly means that the letter case and special symbols are also taken into account:print(pd.Series(['fox', 'koala', 'panda']).is_monotonic) print(pd.Series(['FOX', 'Fox', 'fox']).is_monotonic) print(pd.Series(['*', '&', '_']).is_monotonic) Output: True True FalseA curious exception happens when all the values of a Series are the same. In this case, both methods return True:print(pd.Series([1, 1, 1, 1, 1]).is_monotonic) print(pd.Series(['fish', 'fish']).is_monotonic_decreasing) Output: True TrueAlso Read: 4 Must-Know Python Pandas Functions for Time Series Analysis4. hasnansThis method checks if a Series contains NaN values:import numpy as np print(pd.Series([1, 2, 3, np.nan]).hasnans) print(pd.Series([1, 2, 3, 10, 20]).hasnans) Output: True False5. emptySometimes, we might want to know if a Series is completely empty, not containing even NaN values:print(pd.Series().empty) print(pd.Series(np.nan).empty) Output: True FalseA Series can become empty after some manipulations with it, for example, filtering:s = pd.Series([1, 2, 3]) s[s > 3].empty Output: True 6 & 7. first_valid_index() and last_valid_index()These 2 methods return index for first/last non-NaN value and are particularly useful for Series objects with many NaNs:print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).first_valid_index()) print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).last_valid_index()) Output: 2 4If all the values of a Series are NaN, both methods return None:print(pd.Series([np.nan, np.nan, np.nan]).first_valid_index()) print(pd.Series([np.nan, np.nan, np.nan]).last_valid_index()) Output: None None 8. truncate()This method allows truncating a Series before and after some index value. Let’s truncate the Series from the previous section leaving only non-NaN values:s = pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]) s.truncate(before=2, after=4) Output: 2 1.0 3 2.0 4 3.0 dtype: float64The original index of the Series was preserved. We may want to reset it and also to assign the truncated Series to a variable:s_truncated = s.truncate(before=2, after=4).reset_index(drop=True) print(s_truncated) Output: 0 1.0 1 2.0 2 3.0 dtype: float64Also Read: Pandas vs SQL. When Data Scientists Should Use One Over the Other9. convert_dtypes()As the pandas documentation says, this method is used toConvert columns to best possible dtypes using dtypes supporting pd.NA.If to consider only Series objects and not DataFrames, the only application of this method is to convert all nullable integers (i.e. float numbers with a decimal part equal to 0, such as 1.0, 2.0, etc.) back to “normal” integers. Such float numbers appear when the original Series contains both integers and NaN values. Since NaN is a float in numpy and pandas, it leads to the whole Series with any missing values to become of float type as well.Let’s take a look at the example from the previous section to see how it works:print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan])) print('\n') print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).convert_dtypes()) Output: 0 NaN 1 NaN 2 1.0 3 2.0 4 3.0 5 NaN dtype: float64 0 <NA> 1 <NA> 2 1 3 2 4 3 5 <NA> dtype: Int64 10. clip()We can clip all the values of a Series at input thresholds (lower and upper parameters):s = pd.Series(range(1, 11)) print(s) s_clipped = s.clip(lower=2, upper=7) print(s_clipped) Output: 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 dtype: int64 0 2 1 2 2 3 3 4 4 5 5 6 6 7 7 7 8 7 9 7 dtype: int64 11. rename_axis()In the case of a Series object, this method sets the name of the index:s = pd.Series({'flour': '300 g', 'butter': '150 g', 'sugar': '100 g'}) print(s) s=s.rename_axis('ingredients') print(s) Output: flour 300 g butter 150 g sugar 100 g dtype: object ingredients flour 300 g butter 150 g sugar 100 g dtype: object 12 & 13. nsmallest() and nlargest()These 2 methods return the smallest/largest elements of a Series. By default, they return 5 values, in ascending order for nsmallest() and in descending - for nlargest().s = pd.Series([3, 2, 1, 100, 200, 300, 4, 5, 6]) s.nsmallest() Output: 2 1 1 2 0 3 6 4 7 5 dtype: int64It’s possible to specify another number of the smallest/largest values to be returned. Also, we may want to reset the index and assign the result to a variable:largest_3 = s.nlargest(3).reset_index(drop=True) print(largest_3) Output: 0 300 1 200 2 100 dtype: int64Also Read: Pandas vs SQL. When Data Scientists Should Use One Over the Other14. pct_change()For a Series object, we can calculate percentage change (or, more precisely, fraction change) between the current and a prior element. This approach can be helpful, for example, when working with time series, or for creating a waterfall chart in % or fractions.s = pd.Series([20, 33, 14, 97, 19]) s.pct_change() Output: 0 NaN 1 0.650000 2 -0.575758 3 5.928571 4 -0.804124 dtype: float64To make the resulting Series more readable, let’s round it:s.pct_change().round(2) Output: 0 NaN 1 0.65 2 -0.58 3 5.93 4 -0.80 dtype: float64 15. explode()This method transforms each list-like element of a Series (lists, tuples, sets, Series, ndarrays) to a row. Empty list-likes will be transformed in a row with NaN. To avoid repeated indices in the resulting Series, it’s better to reset index:s = pd.Series([[np.nan], {1, 2}, 3, (4, 5)]) print(s) s_exploded = s.explode().reset_index(drop=True) print(s_exploded) Output: 0 [nan] 1 {1, 2} 2 3 3 (4, 5) dtype: object 0 NaN 1 1 2 2 3 3 4 4 5 5 dtype: object 16. repeat()This method is used for consecutive repeating each element of a Series a defined number of times. Also in this case, it makes sense to reset index:s = pd.Series([1, 2, 3]) print(s) s_repeated = s.repeat(2).reset_index(drop=True) print(s_repeated) Output: 0 1 1 2 2 3 dtype: int64 0 1 1 1 2 2 3 2 4 3 5 3 dtype: int64If the number of repetitions is assigned to 0, an empty Series will be returned:s.repeat(0) Output: Series([], dtype: int64) ConclusionTo sum up, we investigated 16 rarely used pandas methods for working with Series and some of their application cases. If you know some other interesting ways to manipulate pandas Series, you’re very welcome to share them in the comments.Thanks for reading!Also read: Using Python And Pandas Datareader to Analyze Financial Data

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