{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Federico Trotta",
  "title_listed": "Beyond Numpy and Pandas: Unlocking the Potential of Lesser-Known Python Libraries",
  "keywords": "Python库,科学计算",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/beyond-numpy-and-pandas-unlocking-the-potential-of-lesser-known-python-libraries-86d2bdc4d230/",
  "publish_date": "2023-07",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "f3dec1bb3f06",
  "collected_at": "2026-10-02T00:40:13",
  "title": "Beyond Numpy and Pandas: Unlocking the Potential of Lesser-Known Python Libraries | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Beyond Numpy and Pandas: Unlocking the Potential of Lesser-Known Python Libraries\n3 Python libraries for scientific computation you should know as a data professional\n[Federico Trotta](https://towardsdatascience.com/author/federicotrotta/)\nJuly 6, 202312 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1n9QwriSIPVYdYFZWo8cyLA.jpeg)\nPython is one of the most used programming languages in the world and provides developers with a wide range of libraries.\nAnyway, when it comes to data manipulation and scientific computation, we generally think of libraries such as `Numpy`, `Pandas`, or `SciPy`.\nIn this article, we introduce 3 Python libraries you may be interested in.\n* * *\n## 1. Dask\n### Introducing Dask\nDask is a flexible parallel computing library that enables distributed computing and parallelism for large-scale data processing.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\nSo, why should we use Dask? As they say on [their website](https://www.dask.org/):\n> Python has grown to become the dominant language both in data analytics and general programming. This growth has been fueled by computational libraries like NumPy, pandas, and scikit-learn. However, these packages weren't designed to scale beyond a single machine. Dask was developed to natively scale these packages and the surrounding ecosystem to multi-core machines and distributed clusters when datasets exceed memory.\nSo, one of the common uses of Dask, [as they say](https://docs.dask.org/en/latest/dataframe.html), is:\n> Dask DataFrame is used in situations where pandas is commonly needed, usually when pandas fails due to data size or computation speed:\n>   * Manipulating large datasets, even when those datasets don't fit in memory\n>   * Accelerating long computations by using many cores\n>   * Distributed computing on large datasets with standard pandas operations like groupby, join, and time series computations\n> \n\nSo, Dask is a good choice when we need to deal with huge Pandas data frames. This is because Dask:\n> Allows users to manipulate 100GB+ datasets on a laptop or 1TB+ datasets on a workstation\nWhich is a pretty impressive result.\nWhat happens under the hood, is that:\n> Dask DataFrames coordinate many pandas DataFrames/Series arranged along the index. A Dask DataFrame is partitioned _row-wise_ , grouping rows by index value for efficiency. These pandas objects may live on disk or on other machines.\nSo, we have something like that:\n### Some features of Dask in action\nFirst of all, we need to install Dask. We can do it via `pip` or `conda` like so:\ntext\n\n```\n$ pip install dask[complete]\n\nor\n\n$ conda install dask\n```\n\n**FEATURE ONE: OPENING A CSV FILE**\nThe first feature we can show of Dask is how we can open a CSV. We can do it like so:\ntext\n\n```\nimport dask.dataframe as dd\n\n# Load a large CSV file using Dask\ndf_dask = dd.read_csv('my_very_large_dataset.csv')\n\n# Perform operations on the Dask DataFrame\nmean_value_dask = df_dask['column_name'].mean().compute()\n```\n\nSo, as we can see in the code, the way we use Dask is very similar to Pandas. In particular:\n  * We use the method `read_csv()` exactly as in Pandas\n  * We intercept a column exactly as in Pandas. In fact, if we had a Pandas data frame called `df` we'd intercept a column this way: `df['column_name']`.\n  * We apply the `mean()` method to the intercepted column similar to Pandas, but here we also need to add the method `compute()`.\n\n\nAlso, even if the methodology of opening a CSV file it's the same as in Pandas, under the hood Dask is effortlessly processing a large dataset that exceeds the memory capacity of a single machine.\nThis means that we can't see any actual difference, except the fact that a large data frame can't be opened in Pandas, but in Dask we can.\n**FEATURE TWO: SCALING MACHINE LEARNING WORKFLOWS**\nWe can use Dask to also create a classification dataset with a huge number of samples. We can then split it into the train and the test sets, fit the train set with an ML model, and calculate predictions for the test set.\nWe can do it like so:\ntext\n\n```\nimport dask_ml.datasets as dask_datasets\nfrom dask_ml.linear_model import LogisticRegression\nfrom dask_ml.model_selection import train_test_split\n\n# Load a classification dataset using Dask\nX, y = dask_datasets.make_classification(n_samples=100000, chunks=1000)\n\n# Split the data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\n# Train a logistic regression model in parallel\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# Predict on the test set\ny_pred = model.predict(X_test).compute()\n```\n\nThis example stresses the ability of Dask to handle huge datasets even in the case of a Machine Learning problem, by distributing computations across multiple cores.\nIn particular, we can create a \"Dask dataset\" for a classification case with the method `dask_datasets.make_classification()`, and we can specify the number of samples and chunks (even, very huge!).\nSimilarly as before, the predictions are obtained with the method `compute()`.\ntext\n\n```\nNOTE:\n\nin this case, you may need to intsall the module dask_ml.\nYou can do it like so:\n\n$ pip install dask_ml\n```\n\n**FEATURE THREE: EFFICIENT IMAGE PROCESSING**\nThe power of parallel processing that Dask utilizes can also be applied to images.\nIn particular, we could open multiple images, resize them, and save them resized. We can do it like so:\ntext\n\n```\nimport dask.array as da\nimport dask_image.imread\nfrom PIL import Image\n\n# Load a collection of images using Dask\nimages = dask_image.imread.imread('image*.jpg')\n\n# Resize the images in parallel\nresized_images = da.stack([da.resize(image, (300, 300)) for image in images])\n\n# Compute the result\nresult = resized_images.compute()\n\n# Save the resized images\nfor i, image in enumerate(result):\n    resized_image = Image.fromarray(image)\n    resized_image.save(f'resized_image_{i}.jpg')\n```\n\nSo, here's the process:\n  1. We open all the \".jpg\" images in the current folder (or in a folder that you can specify) with the method `dask_image.imread.imread(\"image*.jpg\")`.\n  2. We resize them all at 300x300 using a list comprehension in the method `da.stack()`.\n  3. We compute the result with the method `compute()`, as we did before.\n  4. We save all the resized images with the for cycle.\n\n\n* * *\n## 2. SymPy\n### Introducing Sympy\nIf you need to make mathematical calculations and computations and want to stick to Python, you can try Sympy.\nIndeed: why use other tools and software, when we can use our beloved Python?\nAs per what they write [on their website](https://www.sympy.org/en/index.html), Sympy is:\n> a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.\nBut why use SymPy? They suggest:\n> SymPy is...\n> **- Free:** Licensed under BSD, SymPy is free both as in speech and as in beer.\n> **- Python-based:** SymPy is written entirely in Python and uses Python for its language.\n> **- Lightweight:** SymPy only depends on mpmath, a pure Python library for arbitrary floating point arithmetic, making it easy to use.\n> **- A library:** Beyond use as an interactive tool, SymPy can be embedded in other applications and extended with custom functions.\nSo, it basically has all the characteristics that can be loved by Python addicts!\nNow, let's see some of its features.\n### Some features of SymPy in action\nFirst of all, we need to install it:\ntext\n\n```\n$ pip install sympy\n```\n\ntext\n\n```\nPAY ATTENTION:\n\nif you write $ pip install simpy you'll install another (completely\ndifferent!) library.\n\nSo, the second letter is a \"y\", not an \"i\".\n```\n\n**FEATURE ONE: SOLVING AN ALGEBRAIC EQUATION**\nIf we need to solve an algebraic equation, we can use SymPy like so:\ntext\n\n```\nfrom sympy import symbols, Eq, solve\n\n# Define the symbols\nx, y = symbols('x y')\n\n# Define the equation\nequation = Eq(x**2 + y**2, 25)\n\n# Solve the equation\nsolutions = solve(equation, (x, y))\n\n# Print solution\nprint(solutions)\n\n>>>\n\n[(-sqrt(25 - y**2), y), (sqrt(25 - y**2), y)]\n```\n\nSo, that's the process:\n  1. We define the symbols of the equation with the method `symbols()`.\n  2. We write the algebraic equation with the method `Eq`.\n  3. We solve the equation with the method `solve()`.\n\n\nWhen I was at the University I used different tools to solve these kinds of problems, and I have to say that SymPy, as we can see, is very readable and user-friendly.\nBut, indeed: it's a Python library, so how could that be any different?\n**FEATURE TWO: CALCULATING DERIVATIVES**\nCalculating derivatives is another task we may mathematically need, for a lot of reasons when analyzing data. Often, we may need calculations for any reason, and SympY really simplifies this process. In fact, we can do it like so:\ntext\n\n```\nfrom sympy import symbols, diff\n\n# Define the symbol\nx = symbols('x')\n\n# Define the function\nf = x**3 + 2*x**2 + 3*x + 4\n\n# Calculate the derivative\nderivative = diff(f, x)\n\n# Print derivative\nprint(derivative)\n\n>>>\n\n3*x**2 + 4*x + 3\n```\n\nSo, as we can see, the process is very simple and self-explainable:\n  1. We define the symbol of the function we're deriving with `symbols()`.\n  2. We define the function.\n  3. We calculate the derivative with `diff()` specifying the function and the symbol we're calculating the derivative (this is an absolute derivative, but we could perform even partial derivatives in the case of functions that have `x` and `y` variables).\n\n\nAnd if we test it, we'll see that the result arrives in a matter of 2 or 3 seconds. So, it's also pretty fast.\n**FEATURE THREE: CALCULATING INTEGRATIONS**\nOf course, if SymPy can calculate derivatives, it can also calculate integrations. Let's do it:\ntext\n\n```\nfrom sympy import symbols, integrate, sin\n\n# Define the symbol\nx = symbols('x')\n\n# Perform symbolic integration\nintegral = integrate(sin(x), x)\n\n# Print integral\nprint(integral)\n\n>>>\n\n-cos(x)\n```\n\nSo, here we use the method `integrate()`, specifying the function to integrate and the variable of integration.\nCouldn't it be easier?!\n* * *\n## 3. Xarray\n### Introducing Xarray\nXarray is a Python library that extends the features and functionalities of NumPy, giving us the possibility to work with labeled arrays and datasets.\nAs they say [on their website](https://docs.xarray.dev/en/stable/), in fact:\n> Xarray makes working with labeled multi-dimensional arrays in Python simple, efficient, and fun!\nAnd [also](https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray.html):\n> Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like multidimensional arrays, which allows for a more intuitive, more concise, and less error-prone developer experience.\nIn other words, it extends the functionality of NumPy arrays by adding labels or coordinates to the array dimensions. These labels provide metadata and enable more advanced analysis and manipulation of multi-dimensional data.\nFor example, in NumPy, arrays are accessed using integer-based indexing.\nIn Xarray, instead, each dimension can have a label associated with it, making it easier to understand and manipulate the data based on meaningful names.\nFor example, instead of accessing data with `arr[0, 1, 2]`, we can use `arr.sel(x=0, y=1, z=2)` in Xarray, where `x`, `y`, and `z` are dimension labels.\nThis makes the code much more readable!\nSo, let's see some features of Xarray.\n### Some features of Xarray in action\nAs usual, to install it:\ntext\n\n```\n$ pip install xarray\n```\n\n**FEATURE ONE: WORKING WITH LABELED COORDINATES**\nSuppose we want to create some data related to temperature and we want to label these with coordinates like latitude and longitude. We can do it like so:\ntext\n\n```\nimport xarray as xr\nimport numpy as np\n\n# Create temperature data\ntemperature = np.random.rand(100, 100) * 20 + 10\n\n# Create coordinate arrays for latitude and longitude\nlatitudes = np.linspace(-90, 90, 100)\nlongitudes = np.linspace(-180, 180, 100)\n\n# Create an Xarray data array with labeled coordinates\nda = xr.DataArray(\n    temperature,\n    dims=['latitude', 'longitude'],\n    coords={'latitude': latitudes, 'longitude': longitudes}\n)\n\n# Access data using labeled coordinates\nsubset = da.sel(latitude=slice(-45, 45), longitude=slice(-90, 0))\n```\n\nAnd if we print them we get:\ntext\n\n```\n# Print data\nprint(subset)\n\n>>>\n<xarray.DataArray (latitude: 50, longitude: 25)>\narray([[13.45064786, 29.15218061, 14.77363206, ..., 12.00262833,\n        16.42712411, 15.61353963],\n       [23.47498117, 20.25554247, 14.44056286, ..., 19.04096482,\n        15.60398491, 24.69535367],\n       [25.48971105, 20.64944534, 21.2263141 , ..., 25.80933737,\n        16.72629302, 29.48307134],\n       ...,\n       [10.19615833, 17.106716  , 10.79594252, ..., 29.6897709 ,\n        20.68549602, 29.4015482 ],\n       [26.54253304, 14.21939699, 11.085207  , ..., 15.56702191,\n        19.64285595, 18.03809074],\n       [26.50676351, 15.21217526, 23.63645069, ..., 17.22512125,\n        13.96942377, 13.93766583]])\nCoordinates:\n  * latitude   (latitude) float64 -44.55 -42.73 -40.91 ... 40.91 42.73 44.55\n  * longitude  (longitude) float64 -89.09 -85.45 -81.82 ... -9.091 -5.455 -1.818\n\n```\n\nSo, let's see the process step-by-step:\n  1. We've created the temperature values as a NumPy array.\n  2. We've defined the latitudes and longitueas values as NumPy arrays.\n  3. We've stored all the data in an Xarray array with the method `DataArray()`.\n  4. We've selected a subset of the latitudes and longitudes with the method `sel()` that selects the values we want for our subset.\n\n\nThe result is also easily readable, so labeling is really helpful in a lot of cases.\n**FEATURE TWO: HANDLING MISSING DATA**\nSuppose we're collecting data related to temperatures during the year. We want to know if we have some null values in our array. Here's how we can do so:\ntext\n\n```\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\n\n# Create temperature data with missing values\ntemperature = np.random.rand(365, 50, 50) * 20 + 10\ntemperature[0:10, :, :] = np.nan  # Set the first 10 days as missing values\n\n# Create time, latitude, and longitude coordinate arrays\ntimes = pd.date_range('2023-01-01', periods=365, freq='D')\nlatitudes = np.linspace(-90, 90, 50)\nlongitudes = np.linspace(-180, 180, 50)\n\n# Create an Xarray data array with missing values\nda = xr.DataArray(\n    temperature,\n    dims=['time', 'latitude', 'longitude'],\n    coords={'time': times, 'latitude': latitudes, 'longitude': longitudes}\n)\n\n# Count the number of missing values along the time dimension\nmissing_count = da.isnull().sum(dim='time')\n\n# Print missing values\nprint(missing_count)\n\n>>>\n\n<xarray.DataArray (latitude: 50, longitude: 50)>\narray([[10, 10, 10, ..., 10, 10, 10],\n       [10, 10, 10, ..., 10, 10, 10],\n       [10, 10, 10, ..., 10, 10, 10],\n       ...,\n       [10, 10, 10, ..., 10, 10, 10],\n       [10, 10, 10, ..., 10, 10, 10],\n       [10, 10, 10, ..., 10, 10, 10]])\nCoordinates:\n  * latitude   (latitude) float64 -90.0 -86.33 -82.65 ... 82.65 86.33 90.0\n  * longitude  (longitude) float64 -180.0 -172.7 -165.3 ... 165.3 172.7 180.0\n```\n\nAnd so we obtain that we have 10 null values.\nAlso, if we take a look closely at the code, we can see that we can apply Pandas' methods to an Xarray like `isnull.sum()`, as in this case, that counts the total number of missing values.\n**FEATURE ONE: HANDLING AND ANALYZING MULTI-DIMENSIONAL DATA**\nThe temptation to handle and analyze multi-dimensional data is high when we have the possibility to label our arrays. So, why not try it?\nFor example, suppose we're still collecting data related to temperatures at certain latitudes and longitudes.\nWe may want to calculate the mean, the max, and the median temperatures. We can do it like so:\ntext\n\n```\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\n\n# Create synthetic temperature data\ntemperature = np.random.rand(365, 50, 50) * 20 + 10\n\n# Create time, latitude, and longitude coordinate arrays\ntimes = pd.date_range('2023-01-01', periods=365, freq='D')\nlatitudes = np.linspace(-90, 90, 50)\nlongitudes = np.linspace(-180, 180, 50)\n\n# Create an Xarray dataset\nds = xr.Dataset(\n    {\n        'temperature': (['time', 'latitude', 'longitude'], temperature),\n    },\n    coords={\n        'time': times,\n        'latitude': latitudes,\n        'longitude': longitudes,\n    }\n)\n\n# Perform statistical analysis on the temperature data\nmean_temperature = ds['temperature'].mean(dim='time')\nmax_temperature = ds['temperature'].max(dim='time')\nmin_temperature = ds['temperature'].min(dim='time')\n\n# Print values \nprint(f\"mean temperature:n {mean_temperature}n\")\nprint(f\"max temperature:n {max_temperature}n\")\nprint(f\"min temperature:n {min_temperature}n\")\n\n>>>\n\nmean temperature:\n <xarray.DataArray 'temperature' (latitude: 50, longitude: 50)>\narray([[19.99931701, 20.36395016, 20.04110699, ..., 19.98811842,\n        20.08895803, 19.86064693],\n       [19.84016491, 19.87077812, 20.27445405, ..., 19.8071972 ,\n        19.62665953, 19.58231185],\n       [19.63911165, 19.62051976, 19.61247548, ..., 19.85043831,\n        20.13086891, 19.80267099],\n       ...,\n       [20.18590514, 20.05931149, 20.17133483, ..., 20.52858247,\n        19.83882433, 20.66808513],\n       [19.56455575, 19.90091128, 20.32566232, ..., 19.88689221,\n        19.78811145, 19.91205212],\n       [19.82268297, 20.14242279, 19.60842148, ..., 19.68290006,\n        20.00327294, 19.68955107]])\nCoordinates:\n  * latitude   (latitude) float64 -90.0 -86.33 -82.65 ... 82.65 86.33 90.0\n  * longitude  (longitude) float64 -180.0 -172.7 -165.3 ... 165.3 172.7 180.0\n\nmax temperature:\n <xarray.DataArray 'temperature' (latitude: 50, longitude: 50)>\narray([[29.98465531, 29.97609171, 29.96821276, ..., 29.86639343,\n        29.95069558, 29.98807808],\n       [29.91802049, 29.92870312, 29.87625447, ..., 29.92519055,\n        29.9964299 , 29.99792388],\n       [29.96647016, 29.7934891 , 29.89731136, ..., 29.99174546,\n        29.97267052, 29.96058079],\n       ...,\n       [29.91699117, 29.98920555, 29.83798369, ..., 29.90271746,\n        29.93747041, 29.97244906],\n       [29.99171911, 29.99051943, 29.92706773, ..., 29.90578739,\n        29.99433847, 29.94506567],\n       [29.99438621, 29.98798699, 29.97664488, ..., 29.98669576,\n        29.91296382, 29.93100249]])\nCoordinates:\n  * latitude   (latitude) float64 -90.0 -86.33 -82.65 ... 82.65 86.33 90.0\n  * longitude  (longitude) float64 -180.0 -172.7 -165.3 ... 165.3 172.7 180.0\n\nmin temperature:\n <xarray.DataArray 'temperature' (latitude: 50, longitude: 50)>\narray([[10.0326431 , 10.07666029, 10.02795524, ..., 10.17215336,\n        10.00264909, 10.05387097],\n       [10.00355858, 10.00610942, 10.02567816, ..., 10.29100316,\n        10.00861792, 10.16955806],\n       [10.01636216, 10.02856619, 10.00389027, ..., 10.0929342 ,\n        10.01504103, 10.06219179],\n       ...,\n       [10.00477003, 10.0303088 , 10.04494723, ..., 10.05720692,\n        10.122994  , 10.04947012],\n       [10.00422182, 10.0211205 , 10.00183528, ..., 10.03818058,\n        10.02632697, 10.06722953],\n       [10.10994581, 10.12445222, 10.03002468, ..., 10.06937041,\n        10.04924046, 10.00645499]])\nCoordinates:\n  * latitude   (latitude) float64 -90.0 -86.33 -82.65 ... 82.65 86.33 90.0\n  * longitude  (longitude) float64 -180.0 -172.7 -165.3 ... 165.3 172.7 180.0\n```\n\nAnd we obtained what we wanted, also in a clearly readable way.\nAnd again, as before, to calculate the max, min, and mean values of temperatures we've used Pandas' functions applied to an array.\n* * *\n## Conclusions\nIn this article, we've shown three libraries for scientific calculation and computation.\nWhile SymPy can be the substitute for other tools and software, giving us the possibility to use Python code to compute mathematical calculations, Dask and Xarray extend the functionalities of other libraries, helping us in situations where we may have difficulties with other most known Python libraries for data analysis and manipulation.\n* * *\nHi, I'm Federico Trotta and I'm a freelance Technical Writer.\nWant to collaborate with me? 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