diff --git a/numpy_and_pandas/figures/row_column_ordering.png b/numpy_and_pandas/figures/row_column_ordering.png new file mode 100644 index 0000000..6fdf4cb Binary files /dev/null and b/numpy_and_pandas/figures/row_column_ordering.png differ diff --git a/numpy_and_pandas/numpy_and_pandas.ipynb b/numpy_and_pandas/numpy_and_pandas.ipynb new file mode 100644 index 0000000..52e67c3 --- /dev/null +++ b/numpy_and_pandas/numpy_and_pandas.ipynb @@ -0,0 +1,712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# NumPy and pandas - Crucial Tools for Data Scientists\n", + "\n", + "When it comes to scientific computing and data science, two key python packages are NumPy and pandas. NumPy is a powerful python library that expands Python's functionality by allowing users to create multi-dimenional array objects (`ndarray`). In addition to the creation of `ndarray` objects, NumPy provides a large set of mathematical functions that can operate quickly on the entries of the `ndarray` without the need of for loops. \n", + "Below is an example of the usage of NumPy. The code creates a random array and calculates the cosine for each entry." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.95819067 0.60474588]\n", + " [ 0.78863282 0.95135038]\n", + " [ 0.82418621 0.93289855]\n", + " [ 0.67706351 0.83420891]]\n", + "\n", + " The dimension of y is (4, 2)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "X = np.random.random((4, 2)) # create random 4x2 array\n", + "y = np.cos(X) # take the cosine on each entry of X\n", + "\n", + "print y\n", + "print \"\\n The dimension of y is\", y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can easily access entries of an array, call individual elements, and select certain rows and columns." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.95819067 0.60474588]\n", + "[ 0.60474588 0.95135038 0.93289855 0.83420891]\n", + "0.932898546321\n", + "[[ 0.78863282 0.95135038]]\n" + ] + } + ], + "source": [ + "print y[0, :] # select 1st row\n", + "print y[:, 1] # select 1st column\n", + "print y[2, 1] # select element y_12\n", + "print y[1:2, :] # select rows 2nd and 3rd row" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The pandas (PANel + DAta) Python library allows for easy and fast data analysis and manipulation tools by providing numerical tables and time series data structures called `DataFrame` and `Series`, respectively. Pandas was created to do the following:\n", + "\n", + "- provide data structures that can handle both time and non-time series data\n", + "- allow mathematical operations on the data structures, ignoring the metadata of the data structures\n", + "- use relational operations like those found in programming languages like SQL (join, group by, etc.)\n", + "- handle missing data\n", + "\n", + "Below is an example of the usage of pandas and some of its capabilitites." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Electoral VotesPopulationStateWest of Mississippi
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" + ], + "text/plain": [ + " Electoral Votes Population State West of Mississippi\n", + "0 38 27860000.0 Texas True\n", + "1 3 1060000.0 Rhode Island False\n", + "2 5 1910000.0 Nebraska True" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# create data\n", + "states = ['Texas', 'Rhode Island', 'Nebraska'] # string\n", + "population = [27.86E6, 1.06E6, 1.91E6] # float\n", + "electoral_votes = [38, 3, 5] # integer\n", + "is_west_of_MS = [True, False, True] # Boolean\n", + "\n", + "# create and display DataFrame\n", + "headers = ('State', 'Population', 'Electoral Votes', 'West of Mississippi')\n", + "data = (states, population, electoral_votes, is_west_of_MS)\n", + "data_dict = dict(zip(headers, data))\n", + "\n", + "df1 = pd.DataFrame(data_dict)\n", + "df1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In the above code, we created a pandas `DataFrame` object, a tabular data structure that resembles a spreadsheet like those used in Excel. For those familiar with SQL, you can view a `DataFrame` as an SQL table. The `DataFrame` we created consists of four columns, each with entries of different data types (integer, float, string, and Boolean). \n", + "\n", + "Pandas is built on top of NumPy, relying on `ndarray` and its fast and efficient array based mathematical functions. For example, if we wanted to calculate the mean population across the states, we can run" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10276666.6667\n" + ] + } + ], + "source": [ + "print df1['Population'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Pandas relies on NumPy data types for the entries in the `DataFrame`. Printing the types of individual entries using `iloc` shows" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print type(df1['Electoral Votes'].iloc[0])\n", + "print type(df1['Population'].iloc[0])\n", + "print type(df1['West of Mississippi'].iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Another example of the pandas and NumPy compatibility is if we have a `DataFrame` that is composed of purely numerical data we can apply NumPy functions. For example," + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Electoral VotesPopulationState
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" + ], + "text/plain": [ + " Electoral Votes Population State\n", + "West of Mississippi \n", + "False 3 1060000.0 Rhode Island\n", + "True 38 27860000.0 Texas" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.groupby('West of Mississippi').agg('max')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `apply` method accepts a function to apply to all the entries of a pandas `Series` object. This method is useful for applying a customized function to the entries of a column in a pandas `DataFrame`. For example, we can create a `Series` object that tells us if a state's population is more than two million. The result is a `Series` object that we can append to our original `DataFrame` object." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Electoral VotesPopulationStateWest of MississippiMore than a Million
03827860000.0TexasTrueTrue
131060000.0Rhode IslandFalseFalse
251910000.0NebraskaTrueFalse
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" + ], + "text/plain": [ + " Electoral Votes Population State West of Mississippi \\\n", + "0 38 27860000.0 Texas True \n", + "1 3 1060000.0 Rhode Island False \n", + "2 5 1910000.0 Nebraska True \n", + "\n", + " More than a Million \n", + "0 True \n", + "1 False \n", + "2 False " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "more_than_two_million = df1['Population'].apply(lambda x: x > 2E6) # create Series object of Boolean values\n", + "df1['More than a Million'] = more_than_two_million # append Series object to our original DataFrame\n", + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Accessing columns is inuitive, and returns a pandas `Series` object." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 27860000.0\n", + "1 1060000.0\n", + "2 1910000.0\n", + "Name: Population, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "print df1['Population']\n", + "print type(df1['Population'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "A `DataFrame` is composed of multiple `Series`. The `DataFrame` class resembles a collection of NumPy arrays but with labeled axes and mixed data types across the columns. In fact, `Series` is subclass of NumPy's `ndarray`. While you can achieve the same results of certain pandas methods using NumPy, the result would require more lines of code. Pandas expands on NumPy by providing easy to use methods for data analysis to operate on the `DataFrame` and `Series` classes, which are built on NumPy's powerful `ndarray` class." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## How memory is configured in NumPy\n", + "\n", + "The power of NumPy comes from the `ndarray` class and how it is laid out in memory. The `ndarray` class consists of\n", + "\n", + "- the data type of the entires of the array\n", + "- a pointer to a contiguous block of memory where the data/entries of the array reside\n", + "- a tuple of the array's shape\n", + "- a tuple of the array's stride\n", + "\n", + "The shape refers to the dimension of the array while the stride is the number of bytes to step in a particular dimension when traversing an array in memory. With both the stride and the shape, NumPy has sufficient information to access the array's entries in memory.\n", + "\n", + "By default, NumPy arranges the data in row-major order, like in C. Row-major order lays out the entries of the array by groupings of rows. An alternative is column-major ordering, as used in Fortran and MATLAB, which uses columns as the grouping. NumPy is capable of implementing both ordering schemes by passing the keyword `order` when creating an array. See the figure below for the differeneces in the schemes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The continguous memory layout allows NumPy to use vector processors in modern CPUs and array computations. Array computations are efficient because NumPy can loop through the entries in data properly by knowing the location in memory and the data type of the entries. NumPy can also link to established and highly optimized linear algebra libraries such as BLAS and LAPACK. As you can see, using the NumPy `ndarray` offers more efficient and fast computations over the native Python list. No wonder pandas and other Python libraries are built on top of NumPy. However, the infrastructure of the `ndarray` class must require all entries to be the same data type, something that a Python `list` class is not limited to." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "## Hetereogeneous data types in pandas\n", + "\n", + "As mentioned earlier, the pandas `DataFrame` class can store hetereogeneous data; each column contains a `Series` object of a different data type. The `DataFrame` is stored as several blocks in memory, where each block contains the columns of the `DataFrame` that have the same data type. For example, a `DataFrame` with five columns comprised of two columns of floats, two columns of integers, and one Boolean column will be stored using three blocks. \n", + "\n", + "With the data of the `DataFrame` stored using blocks grouped by data, operations _within_ blocks are effcient, as described previously on why NumPy operations are fast. However, operations involving several blocks will not be efficient. Information on these blocks of a `DataFrame` object can be accessed using `._data`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BlockManager\n", + "Items: Index([u'Electoral Votes', u'Population', u'State', u'West of Mississippi',\n", + " u'More than a Million'],\n", + " dtype='object')\n", + "Axis 1: RangeIndex(start=0, stop=3, step=1)\n", + "FloatBlock: slice(1, 2, 1), 1 x 3, dtype: float64\n", + "IntBlock: slice(0, 1, 1), 1 x 3, dtype: int64\n", + "BoolBlock: slice(3, 4, 1), 1 x 3, dtype: bool\n", + "ObjectBlock: slice(2, 3, 1), 1 x 3, dtype: object\n", + "BoolBlock: slice(4, 5, 1), 1 x 3, dtype: bool" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1._data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The `DataFrame` class can allow columns with mixed data types. For these cases, the data type for the column is referred to as `object`. When the data type is `object`, the data is no longer stored in the NumPy `ndarray` format, but rather a continguous block of pointers where each pointer referrences a Python object. Thus, operations on a `DataFrame` involving `Series` of data type `object` will not be efficient.\n", + "\n", + "Strings are stored in pandas as Python `object` data type. This is because strings have variable memory size. In contrast, integers and floats have a fixed byte size. However, if a `DataFrame` has columns with categorial data, encoding the entries using integers will be more memory and computational efficient. For example, a column containing entries of \"small\", \"medium\", and \"large\" can be coverted to 0, 1, and 2 and the data type of that new column is now an integer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## The importance of understanding Numpy and pandas\n", + "\n", + "Through this article, we have seen \n", + "\n", + "- examples of usage of NumPy and pandas\n", + "- how memory is configured in NumPy\n", + "- how pandas relies on NumPy\n", + "- how pandas deals with hetereogeneous data types\n", + "\n", + "While knowing how NumPy and pandas work is not necessary to use these tools, knowing the working of these libraries and how they are related enables data scientists to effectively yield these tools. More effective use of these tools becomes more important for larger data sets and more complex analysis, where even a small improvement in terms of percentage translates to large time savings." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pandas_vs_PostgreSQL/README.md b/pandas_vs_PostgreSQL/README.md new file mode 100644 index 0000000..79a20e4 --- /dev/null +++ b/pandas_vs_PostgreSQL/README.md @@ -0,0 +1,17 @@ +# pandas vs. PostgreSQL +A benchmark comparing pandas and PostgreSQL for various table sizes and commands. + +## Getting Started +To start the benchmark, run `run_script.sh`. The number of test replicates and size of the table/DataFrame can be adjusted in `run_script.sh`. The table/DataFrame are created from csv files. If these csv files do not exist, `run_script.sh` creates them by calling on `create_dataset.py`. + +### Prerequisites +Apart from having pandas and Postgre installed, you need the following Python packages. + +``` +contexttimer +numpy +psycopg2 +``` + +### The Benchmark +The benchmark is performed for following tasks: load csv, select column, filter, and group by and applying aggregate function. The results are stored as a separate JSON files for pandas and Postgre. The JSON file contains the results for each task and for each table size. A write-up of the results are found in the `analysis_writeup.ipynb` notebook. \ No newline at end of file diff --git a/pandas_vs_PostgreSQL/analysis_writeup.ipynb b/pandas_vs_PostgreSQL/analysis_writeup.ipynb new file mode 100644 index 0000000..ef04e23 --- /dev/null +++ b/pandas_vs_PostgreSQL/analysis_writeup.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# pandas vs PostgreSQL\n", + "\n", + "Working on large data science projects usually involves the user accessing, manipulating, and retrieving data on a server. Next, the work flow moves client-side where the user will apply more refined data analysis and processing, typically tasks not possible or too clumsy to be done on the server. SQL (Structured Query Language) is ubiquitous in industry and data scientists will have to use it in their work to access data on the server.\n", + "\n", + "The line between what data manipulation should be done server-side using SQL or on the client-side using a language like Python is not clear. Further, people who are either uncomfortable or dislike using SQL may be tempted to keep server-side manipulation to a minimum and reserve more of those actions on the client-side. With powerful and popular Python libraries for data wrangling and manipulation, the temptation to keep server-side processing to a minimum has increased.\n", + "\n", + "This article will compare the execution time for several typical data manipulation tasks such as join and group by using PostgreSQL and pandas. PostgreSQL, often shortened as Postgres, is an object-relational database management system. It is free and open-source and runs on all major operating systems. Pandas is a Python data manipulation library that offers data structures akin to Excel spreadsheets and SQL tables and functions for manipulating those data structures.\n", + "\n", + "The performance will be measured for both tools for the following actions:\n", + "\n", + "- select columns\n", + "- filter rows\n", + "- group by and aggregation\n", + "- load a large CSV file\n", + "- join two tables\n", + "\n", + "How these tasks scale as a function of table size will be explored by running the analysis with datasets with ten to ten million rows. These datasets are stored as CSV files and have four columns; the entries of the first two columns are floats, the third are strings, while the last are integers representing a unique id. For joining two tables, a second dataset is used, a column of integer ids and a column of floats.\n", + "\n", + "For each of the five tasks listed above, the benchmark test was run for one hundred replicates for each dataset size. The Postgres part of the benchmark was performed using pgbench, a commandline program for running benchmark tests of Postgres. It can accept custom scripts containing SQL queries to perform the benchmark. The computer used for this study runs Ubuntu 16.04, with 16 GB of RAM, and an 8 core process at 1.8 GHz. The code used for this benchmark can be found on [GitHub](https://github.com/xofbd/pandas_vs_PostgreSQL). The repository contains all the code to run the benchmark, the results as JSON files, and figures plotting the comparison of the two methods.\n", + "\n", + "It is important for data scientists to know the limitations of their tools and what approaches are optimal in terms of time. Although smaller projects will not benefit a lot from speed up, small percentage gains in more data intensive applications will translate into large absolute time savings." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Benchmark Results\n", + "\n", + "The benchmarking relied on two datasets, referred to as A and B. The headers and data types (in paranthesis) for the columns of dataset A are \"score_1\" (float), \"score_2\" (float), \"id\" (integer), and \"section\" (string)\". For dataset B, the headers and data types are \"score_3\" (float) and \"id\" (integer). The \"id\" column relates the two datasets together. The figures below show the mean execution time as a function of the number of rows in the datasets, using a log-log axis. Below each figure, a description of the task and the code used for each tool is provided.\n", + "\n", + "\n", + "For selecting columns, one column from the table/DataFrame was returned. The code for this task are\n", + "
__pandas__: `df_A['score_1']`\n", + "
__Postgres__: `SELECT score_1 FROM test_table_A;`\n", + "\n", + "\n", + "For filtering rows, a table/DataFrame was returned with only rows meeting a criterion. The code for this task are\n", + "
__pandas__: `df_A[self.df_A['section'] == 'A']`\n", + "
__Postgres__: `SELECT * FROM test_table_A WHERE section = 'A';`\n", + "\n", + "\n", + "For grouping and applying aggregation functions, records are grouped by section and mean and maximum score are reported for the two scores. The code for this task are\n", + "
__pandas__: `df_A.groupby('section').agg({'score_1': 'mean', 'score_2': 'max'})`\n", + "
__Postgres__: `SELECT AVG(score_1), MAX(score_2)` \n", + "
`FROM test_table_A` \n", + "
`GROUP BY section;`\n", + "\n", + "\n", + "For joining, the datasets are joined on datasets' id and the resulting table/DataFrame is returned. The code for this task are\n", + "
__pandas__: `df_A.merge(self.df_B, left_on='id', right_on='id')` \n", + "
__Postgres__: `SELECT * FROM test_table_A` \n", + "
`JOIN test_table_B on test_table_A.id = test_table_B.id;`\n", + "\n", + "\n", + "For loading a csv file, dataset A is loaded from disk to create a table/DataFrame. The code for this task are \n", + "
__pandas__: `df_A = pd.read_csv('PATH_TO_CSV_FILE', header=None, index_col=False, names=self.columns_A)`\n", + "
__Postgres__: `COPY test_table_A FROM 'PATH_TO_CSV_FILE' WITH DELIMITER ',';`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "## Conclusions\n", + "\n", + "Overall, pandas outperformed Postgres, often running over five to ten times faster for the larger datasets. The only cases when Postgres performed better were for smaller sized datasets, typically lesss than a thousand rows. Selecting columns was very efficient in pandas, with an O(1) time complexity because the DataFrame is already stored in memory. In general, loading and joining were the tasks that took the longest, requiring times greater than a second for large datasets.\n", + "\n", + "For dataset sizes investigated, pandas is the better tool for the data analysis tasks studied. However, pandas does have its limitations and there is still a need for SQL. For pandas, the data is stored in memory and it will be difficult loading a CSV file greater than half of the system's memory. For the ten-thousand row dataset, the file size was about 400 MB, but the dataset only had four columns. Datasets often contain hundreds of columns, resulting in file sizes on the order of 10 GB when the dataset has over a million rows.\n", + "\n", + "Postgres and pandas are ultimately different tools with overlapping functionality. Postgres and other SQL based languages were created to manage databases and offer users a convenient way to access and retrieve data, especially across multiple tables. The server running Postgres would have all the datasets stored as tables across the system, and it would be impractical for a user to transfer the required tables to their system and use pandas to perform tasks such as join and group by client side. Pandas was created for data manipulation and its strength lies in complex data analysis operations. One should not view pandas and Postgres as competing entities but rather important tools making up the Data Science computational stack." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pandas_vs_PostgreSQL/benchmark_test.py b/pandas_vs_PostgreSQL/benchmark_test.py new file mode 100644 index 0000000..5c33c01 --- /dev/null +++ b/pandas_vs_PostgreSQL/benchmark_test.py @@ -0,0 +1,71 @@ +import re +from contexttimer import Timer +from pandas_tasks import PandasTasks +from postgres_tasks import PostgresTasks + + +def run_test(tool, csv_file_A, csv_file_B, N=10): + """Return dictionary of benchmark results and number of rows in the dataset. + + + Positional arguments: + tool: tool to use for benchmark (pandas or postgres) + csv_file_A: csv file name to use for DataFrame/table A + csv_file_B: csv file name to use for DataFrame/table B + + Keyword arguments: + N: number of test replicates + """ + + # define tool to use + if tool.lower() == 'pandas': + tool_task = PandasTasks(csv_file_A, csv_file_B) + elif tool.lower() in ('postgresql', 'postgres', 'psycopg2'): + tool_task = PostgresTasks(csv_file_A, csv_file_B) + else: + raise ValueError("tool must either be pandas or postgres") + + # loop through each task + tasks = ('select', 'filter', 'groupby_agg', 'join', 'load') + benchmark_dict = {} + num_rows = int(re.findall(r'\d+', csv_file_A)[0]) + + for task in tasks: + print "running " + task + " for " + str(num_rows) + " rows using " + tool + task_time = [] + + for _ in xrange(N): + with Timer() as t: + getattr(tool_task, task)() + task_time.append(t.elapsed) + + benchmark_dict[task] = task_time + + tool_task.clean_up() + + return benchmark_dict, num_rows + +if __name__ == '__main__': + import json + import os + import sys + + tool = sys.argv[1].lower() + num_reps = int(sys.argv[2]) + + # get csv files names + files_A = os.listdir('csv/A') + files_B = os.listdir('csv/B') + files_A.sort() + files_B.sort() + + result_dict = {} + + for f_A, f_B in zip(files_A, files_B): + results, row = run_test( + tool, 'csv/A/' + f_A, 'csv/B/' + f_B, N=num_reps) + result_dict[str(row)] = results + + # dump dictionary to json + with open('results/' + tool + '_benchmark.json', 'w') as f: + json.dump(result_dict, f) diff --git a/pandas_vs_PostgreSQL/create_dataset.py b/pandas_vs_PostgreSQL/create_dataset.py new file mode 100644 index 0000000..1952fc5 --- /dev/null +++ b/pandas_vs_PostgreSQL/create_dataset.py @@ -0,0 +1,29 @@ +import numpy as np +import pandas as pd + + +def create_csv(n=1000): + seed = np.random.seed(1) + + # generate random dataset + columns = ('id', 'section', 'score_1', 'score_2') + labels = ('A', 'B', 'C', 'D') + + id = np.random.choice(range(n), n, replace=False) + section = np.random.choice(labels, n) + score_1 = np.random.rand(n) + score_2 = np.random.rand(n) + score_3 = np.random.rand(n) + + # create and dump DataFrame to csv + df_A = pd.DataFrame(dict(zip(columns, (id, section, score_1, score_2)))) + df_B = pd.DataFrame(dict(zip(('id', 'score_3'), (id, score_3)))) + df_A.to_csv('csv/A/test_A_' + str(n) + + '_rows.csv', index=False, header=False) + df_B.to_csv('csv/B/test_B_' + str(n) + + '_rows.csv', index=False, header=False) + +if __name__ == '__main__': + import sys + + create_csv(int(sys.argv[1])) diff --git a/pandas_vs_PostgreSQL/create_json.py b/pandas_vs_PostgreSQL/create_json.py new file mode 100644 index 0000000..6fc604c --- /dev/null +++ b/pandas_vs_PostgreSQL/create_json.py @@ -0,0 +1,34 @@ +import json +import os +import re +import sys + +csv_files = os.listdir('csv/A/') +log_files = os.listdir('log/') + +num_rows = [re.findall(r'\d+', f)[0] for f in csv_files] +results = {} + +# loop through each number of rows, loading the appropriate log files for each +# task. The 3rd column of each line of the file is the execution time, in +# milliseconds. + +for n in num_rows: + S = '_' + n + '.log' + results_for_row = {} + + for f in [log for log in log_files if S in log]: + task = re.findall(r'(_\w+_)', f)[0][1:-1] + time = [] + + with open('log/' + f, 'r') as file: + for line in file: + time.append(float(line.split(" ")[2]) / 1E6) + + results_for_row[task] = time + + results[n] = results_for_row + +# dump results to JSON +with open('results/postgres_benchmark.json', 'w') as f: + json.dump(results, f) diff --git a/pandas_vs_PostgreSQL/figures/filter_results_plot.png b/pandas_vs_PostgreSQL/figures/filter_results_plot.png new file mode 100644 index 0000000..13c4877 Binary files /dev/null and b/pandas_vs_PostgreSQL/figures/filter_results_plot.png differ diff --git a/pandas_vs_PostgreSQL/figures/groupby_agg_results_plot.png b/pandas_vs_PostgreSQL/figures/groupby_agg_results_plot.png new file mode 100644 index 0000000..6fced00 Binary files /dev/null and b/pandas_vs_PostgreSQL/figures/groupby_agg_results_plot.png differ diff --git a/pandas_vs_PostgreSQL/figures/join_results_plot.png b/pandas_vs_PostgreSQL/figures/join_results_plot.png new file mode 100644 index 0000000..ef1227e Binary files /dev/null and b/pandas_vs_PostgreSQL/figures/join_results_plot.png differ diff --git a/pandas_vs_PostgreSQL/figures/load_results_plot.png b/pandas_vs_PostgreSQL/figures/load_results_plot.png new file mode 100644 index 0000000..225e490 Binary files /dev/null and b/pandas_vs_PostgreSQL/figures/load_results_plot.png differ diff --git a/pandas_vs_PostgreSQL/figures/select_results_plot.png b/pandas_vs_PostgreSQL/figures/select_results_plot.png new file mode 100644 index 0000000..2593b9e Binary files /dev/null and b/pandas_vs_PostgreSQL/figures/select_results_plot.png differ diff --git a/pandas_vs_PostgreSQL/pandas_tasks.py b/pandas_vs_PostgreSQL/pandas_tasks.py new file mode 100644 index 0000000..4905922 --- /dev/null +++ b/pandas_vs_PostgreSQL/pandas_tasks.py @@ -0,0 +1,38 @@ +import pandas as pd + + +class PandasTasks(object): + + def __init__(self, csv_file_A, csv_file_B): + self.csv_file_A = csv_file_A + self.csv_file_B = csv_file_B + self.columns_A = ('id', 'score_1', 'score_2', 'section') + self.columns_B = ('id', 'score_3') + + self.df_A = pd.read_csv(csv_file_A, header=None, index_col=False, + names=self.columns_A) + self.df_B = pd.read_csv(csv_file_B, header=None, index_col=False, + names=self.columns_B) + + def load(self): + self.df_A = pd.read_csv(self.csv_file_A, header=None, index_col=False, + names=self.columns_A) + + def select(self): + self.df_A['score_1'] + + def filter(self): + self.df_A[self.df_A['section'] == 'A'] + + def groupby_agg(self): + self.df_A.groupby('section').agg({'score_1': 'mean', 'score_2': 'max'}) + + def join(self): + self.df_A.merge(self.df_B, left_on='id', right_on='id') + + def get_num_rows(self): + return len(self.df_A) + + def clean_up(self): + del(self.df_A) + del(self.df_B) diff --git a/pandas_vs_PostgreSQL/pgbench_queries.sh b/pandas_vs_PostgreSQL/pgbench_queries.sh new file mode 100755 index 0000000..cf50a28 --- /dev/null +++ b/pandas_vs_PostgreSQL/pgbench_queries.sh @@ -0,0 +1,53 @@ +#!/bin/bash +# +# Run PostgreSQL benchmark using pgbench. The only positional parameter is the +# the number of benchmark replicates. + +# initialize directories and remove old pgbench_log files +if [ ! -d log ]; then + mkdir log +fi + +if [ -e pgbench_log.* ]; then + rm pgbench_log* +fi + +# loop through all csv files +for file_A in csv/A/*; do + + # initialize table and variables + psql -U $USER -d $USER -f queries/init.sql > /dev/null + num_rows=$(echo $file_A | grep -oP '\d+') + + # create and run loading csv query files + file_B="csv/B/test_B_"$num_rows"_rows.csv" + query_A="COPY test_table_A FROM ""'""$PWD"/"$file_A""'"" WITH DELIMITER ',';" + query_B="COPY test_table_B FROM ""'""$PWD"/"$file_B""'"" WITH DELIMITER ',';" + + echo "DELETE FROM test_table_A;" > queries/load_all.sql + echo "DELETE FROM test_table_B;" >> queries/load_all.sql + echo "$query_A" >> queries/load_all.sql + echo "$query_B" >> queries/load_all.sql + + echo "DELETE FROM test_table_A;" > queries/load.sql + echo "$query_A" >> queries/load.sql + + psql -U $USER -d $USER -f queries/load_all.sql > /dev/null + + # benchmark each task + for task in select filter groupby_agg join load; do + echo "running "$task" for "$num_rows" rows using Postgres" + pgbench -ln -t $1 -f queries/$task.sql > /dev/null + mv pgbench_log* log/pgbench_$task"_"$num_rows".log" + done + + # drop tables + psql -U $USER -d $USER -f queries/clean_up.sql +done + +# format results from logs and clean up +python create_json.py + +if [ -d log ]; then + rm -rf log +fi diff --git a/pandas_vs_PostgreSQL/postgres_tasks.py b/pandas_vs_PostgreSQL/postgres_tasks.py new file mode 100644 index 0000000..0aff351 --- /dev/null +++ b/pandas_vs_PostgreSQL/postgres_tasks.py @@ -0,0 +1,81 @@ +import os +import psycopg2 + + +class PostgresTasks(object): + + def __init__(self, csv_file_A, csv_file_B): + self.csv_file_A = csv_file_A + self.csv_file_B = csv_file_B + + # get user name for postgres connection + user = os.popen("echo $USER").read().strip() + dbname = user + + # create psql connection, cursor, and create test table + self.conn = psycopg2.connect(dbname=dbname, user=user) + self.cur = self.conn.cursor() + self.cur.execute("DROP TABLE IF EXISTS test_table_A;") + self.cur.execute("DROP TABLE IF EXISTS test_table_B;") + + query_A = """ + CREATE TABLE test_table_A + (id int, score_1 float, score_2 float, section char(1)); + """ + + query_B = """ + CREATE TABLE test_table_B + (id int, score_3 float); + """ + + self.cur.execute(query_A) + self.cur.execute(query_B) + + # load csv files to tables + with open(self.csv_file_A, 'r') as f: + self.cur.copy_from(f, "test_table_A", sep=',') + + with open(self.csv_file_B, 'r') as f: + self.cur.copy_from(f, "test_table_B", sep=',') + + def load(self): + self.cur.execute("DELETE FROM test_table_A;") + + with open(self.csv_file_A, 'r') as f: + self.cur.copy_from(f, "test_table_A", sep=',') + + def select(self): + self.cur.execute('SELECT score_1 FROM test_table_A;') + + def filter(self): + self.cur.execute( + "SELECT * FROM test_table_A WHERE section = 'A';") + + def groupby_agg(self): + query = """ + SELECT AVG(score_1), MAX(score_2) + FROM test_table_A + GROUP BY section; + """ + + self.cur.execute(query) + + def join(self): + query = """ + SELECT * FROM test_table_A + JOIN test_table_B on test_table_A.id = test_table_B.id; + """ + + self.cur.execute(query) + + def get_num_rows(self): + self.cur.execute("SELECT COUNT(*) FROM test_table;") + num_rows = self.cur.fetchall() + + return int(num_rows[0][0]) + + def clean_up(self): + self.cur.execute("DROP TABLE IF EXISTS test_table_A;") + self.cur.execute("DROP TABLE IF EXISTS test_table_B;") + self.conn.commit() + self.conn.close() diff --git a/pandas_vs_PostgreSQL/queries/clean_up.sql b/pandas_vs_PostgreSQL/queries/clean_up.sql new file mode 100644 index 0000000..6239633 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/clean_up.sql @@ -0,0 +1,2 @@ +DROP TABLE IF EXISTS test_table_A; +DROP TABLE IF EXISTS test_table_B; diff --git a/pandas_vs_PostgreSQL/queries/filter.sql b/pandas_vs_PostgreSQL/queries/filter.sql new file mode 100644 index 0000000..9702a08 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/filter.sql @@ -0,0 +1 @@ +SELECT * FROM test_table_A WHERE section = 'A'; diff --git a/pandas_vs_PostgreSQL/queries/groupby_agg.sql b/pandas_vs_PostgreSQL/queries/groupby_agg.sql new file mode 100644 index 0000000..d8eb816 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/groupby_agg.sql @@ -0,0 +1 @@ +SELECT AVG(score_1), MAX(score_2) FROM test_table_A GROUP BY section; diff --git a/pandas_vs_PostgreSQL/queries/init.sql b/pandas_vs_PostgreSQL/queries/init.sql new file mode 100644 index 0000000..920fce6 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/init.sql @@ -0,0 +1,4 @@ +DROP TABLE IF EXISTS test_table_A; +DROP TABLE IF EXISTS test_table_B; +CREATE TABLE test_table_A (id int, score_1 float, score_2 float, section char(1)); +CREATE TABLE test_table_B (id int, score_3 float); \ No newline at end of file diff --git a/pandas_vs_PostgreSQL/queries/join.sql b/pandas_vs_PostgreSQL/queries/join.sql new file mode 100644 index 0000000..1016544 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/join.sql @@ -0,0 +1 @@ +SELECT * FROM test_table_A JOIN test_table_B on test_table_A.id = test_table_B.id; diff --git a/pandas_vs_PostgreSQL/queries/select.sql b/pandas_vs_PostgreSQL/queries/select.sql new file mode 100644 index 0000000..d7703b3 --- /dev/null +++ b/pandas_vs_PostgreSQL/queries/select.sql @@ -0,0 +1 @@ +SELECT score_1 FROM test_table_A; diff --git a/pandas_vs_PostgreSQL/results/pandas_benchmark.json b/pandas_vs_PostgreSQL/results/pandas_benchmark.json new file mode 100644 index 0000000..7119c9b --- /dev/null +++ b/pandas_vs_PostgreSQL/results/pandas_benchmark.json @@ -0,0 +1 @@ +{"10": {"filter": [0.0009121894836425781, 0.0007190704345703125, 0.0007150173187255859, 0.0007300376892089844, 0.0007688999176025391, 0.0007128715515136719, 0.0007100105285644531, 0.0007040500640869141, 0.0007081031799316406, 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create directories +for dir in csv csv/A csv/B results; do + if [ ! -d $dir ]; then + mkdir $dir + fi +done + +# create test csv files +for n in 10 100 1000 10000 100000 1000000 10000000; do + file_A="csv/A/test_A_"$n"_rows.csv" + file_B="csv/B/test_B_"$n"_rows.csv" + + if [ ! -f $file_A ] || [ ! -f $file_B ]; then + echo "creating $n row dataset" + python create_dataset.py $n + fi +done + +# run pandas benchmark +num_replicates=100 +python benchmark_test.py psycopg2 $num_replicates +python benchmark_test.py pandas $num_replicates +./pgbench_queries.sh $num_replicates diff --git a/pandas_vs_PostgreSQL/save_plots.py b/pandas_vs_PostgreSQL/save_plots.py new file mode 100644 index 0000000..d5eb8e4 --- /dev/null +++ b/pandas_vs_PostgreSQL/save_plots.py @@ -0,0 +1,62 @@ +from collections import OrderedDict +import json +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + + +def calc_stats(data_dict): + num_rows = [int(x) for x in data_dict.keys()] + num_rows.sort() + + tasks = data_dict.values()[0].keys() + task_stats = {} + + for task in tasks: + stats = OrderedDict() + + for row in num_rows: + stats[row] = np.array(data_dict[str(row)][task]).mean() + + task_stats[task] = stats + + return task_stats + +if __name__ == '__main__': + + # load results + with open('results/pandas_benchmark.json', 'r') as f: + pandas_results = json.load(f) + + with open('results/postgres_benchmark.json', 'r') as f: + postgres_results = json.load(f) + + pandas_task_stats = calc_stats(pandas_results) + postgres_task_stats = calc_stats(postgres_results) + title_labels = dict( + zip(['load', 'select', 'filter', 'groupby_agg', 'join'], + ['Load', 'Select', 'Filter', 'Group By and Aggregate', 'Join'])) + + for task in pandas_task_stats.keys(): + x_pandas = pandas_task_stats[task].keys() + y_pandas = pandas_task_stats[task].values() + + x_postgres = postgres_task_stats[task].keys() + y_postgres = postgres_task_stats[task].values() + + f = plt.figure() + plt.loglog( + x_pandas, y_pandas, marker='o', markersize=8, linestyle='--', + linewidth=2) + plt.loglog( + x_postgres, y_postgres, marker='s', markersize=8, linestyle='--', + linewidth=2) + + plt.xlabel('Number of Rows (-)', fontsize=16) + plt.xticks(fontsize=16) + plt.ylabel('Mean Execution Time (seconds)', fontsize=16) + plt.yticks(fontsize=16) + plt.title(title_labels[task], fontsize=16) + plt.legend(['pandas', 'Postgres'], loc='upper left', fontsize=16) + plt.tight_layout() + f.savefig('figures/' + task + '_results_plot.png', dpi=300) diff --git a/pandas_vs_excel/csv/exam_one.csv b/pandas_vs_excel/csv/exam_one.csv new file mode 100644 index 0000000..3bfd4cb --- /dev/null +++ b/pandas_vs_excel/csv/exam_one.csv @@ -0,0 +1,6 @@ +Student Name,Exam 1 Score +Edwin Duncan, 83 +Steve Hayes, 69 +Cecilia Richards, 91 +Dorothy Harris, 53 +Jordan Davis, 76 diff --git a/pandas_vs_excel/csv/exam_two.csv b/pandas_vs_excel/csv/exam_two.csv new file mode 100644 index 0000000..bb2c46e --- /dev/null +++ b/pandas_vs_excel/csv/exam_two.csv @@ -0,0 +1,6 @@ +Student Name,Exam 2 Score +Cecilia Richards, 73 +Dorothy Harris, 75 +Edwin Duncan, 33 +Steve Hayes, 42 +Jordan Davis, 81 \ No newline at end of file diff --git a/pandas_vs_excel/csv/sections.csv b/pandas_vs_excel/csv/sections.csv new file mode 100644 index 0000000..99a5378 --- /dev/null +++ b/pandas_vs_excel/csv/sections.csv @@ -0,0 +1,6 @@ +Student Name,Section +Cecilia Richards,A +Dorothy Harris,B +Edwin Duncan,C +Steve Hayes,A +Jordan Davis,B diff --git a/pandas_vs_excel/pandas_vs_excel.ipynb b/pandas_vs_excel/pandas_vs_excel.ipynb new file mode 100644 index 0000000..d1b7de6 --- /dev/null +++ b/pandas_vs_excel/pandas_vs_excel.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Pandas and Excel\n", + "\n", + "Microsoft Excel is a spreadsheet software, containing data in tabular form. Entries of the data are located in cells, with numbered rows and letter labeled columns. Excel is widespread across industries and has been around for over thirty years. It is often people's first introduction to data analysis. \n", + "\n", + "Most users feel at home using a GUI to operate Excel and no programming is necessary for the most commonly used features. The data is presented right in front of the user and it is easy to scroll around through the spreadsheet. Making plots from the data only involves highlighting cells in the spreadsheet and clicking a few buttons.\n", + "\n", + "There are various shortcomings with Excel. It is closed source and not free. There are free open-source alternatives like OpenOffice and LibreOffice suites, but there might be compatibility issues between file formats, especially for complex spreadsheets. Excel becomes unstable for files reaching 500 MB, being unresponsive and crashing for large files, hindering productivity. Collaborations can become difficult because it is hard to inspect the spreadsheet and understand how certain values are calculated/populated. It is difficult to understand the user's thought process and work flow for the analysis.\n", + "\n", + "The functionality of the spreadsheet is sensitive to the layout and moving entries around can have disastrous effects. Tasks like data cleaning, munging, treating different data types, and handling missing data are often difficult and require manual manipulation. Further, the user is limited to the built-in functionality of the program. In short, Excel is great for certain tasks but becomes unwieldy and inefficient as the tasks become more complicated.\n", + "\n", + "The Python library pandas is a great alternative to Excel, providing much of the same functionality and more. Pandas is great for data manipulation, cleaning, analysis, and exploration. Additionally, these tasks can be easily automated and reapplied to different datasets. It is built on top of Python's NumPy library which offers fast and efficient numerical computations. Unlike Excel, it is open-source and free.\n", + "\n", + "Some people may be intimidated to use Python and pandas because they will have to move away from the point-and-click GUI approach of Excel. Knowing pandas is a great introduction to more powerful and complex data analysis. This article explores how do to some common Excel tasks in pandas, helping people get their feet wet with one of the most powerful Python libraries for data science." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Quick Introduction to pandas\n", + "\n", + "The equivalent to an Excel spreadsheet in pandas is the `DataFrame` class. It looks like a spreadsheet, with rows, columns, and indices. Let us consider three spreadsheets, the first two containing each student's grade on an exam and the third has information on which section the students belong. These `DataFrames` are loaded into memory from CSV files using the `read_csv` function." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Student Name Exam 1 Score\n", + "0 Edwin Duncan 83\n", + "1 Steve Hayes 69\n", + "2 Cecilia Richards 91\n", + "3 Dorothy Harris 53\n", + "4 Jordan Davis 76" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# import pandas library\n", + "import pandas as pd\n", + "\n", + "# create pandas DataFrame from CSV\n", + "exam_one = pd.read_csv('csv/exam_one.csv', delimiter=',')\n", + "exam_two = pd.read_csv('csv/exam_two.csv', delimiter=',')\n", + "sections = pd.read_csv('csv/sections.csv', delimiter=',')\n", + "\n", + "# print the top five entires of the DataFrame\n", + "exam_one.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Columns can be indiviudally selected simply by referring to them by name." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Edwin Duncan\n", + "1 Steve Hayes\n", + "2 Cecilia Richards\n", + "3 Dorothy Harris\n", + "4 Jordan Davis\n", + "Name: Student Name, dtype: object" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exam_one['Student Name']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Vlookup\n", + "\n", + "Experienced Excel users rely on Vlookup, a built-in function that searches (looks up) a specified value in one column and returns the corresponding value of another column. For our example of exam scores, we would like to take a student's second exam score and include it into the table of first exam score. The column of student names may not be in the same order, e.g., the first name in one table may not correpsond to the first name in another table.\n", + "\n", + "A Vlookup can be done in pandas using the `merge` method of a DataFrame. The `merge` method takes the second `DataFrame` and the `on` keyword specifies the column name to perform that matching on. The result is a new `DataFrame` with both exam scores." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Student Name Exam 1 Score Exam 2 Score\n", + "0 Edwin Duncan 83 33\n", + "1 Steve Hayes 69 42\n", + "2 Cecilia Richards 91 73\n", + "3 Dorothy Harris 53 75\n", + "4 Jordan Davis 76 81" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply \"Vlookup\" in padas\n", + "exam_scores = exam_one.merge(exam_two, on='Student Name')\n", + "exam_scores.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Pivot Tables\n", + "\n", + "Pivot tables are used to aggregate and filter data and are a useful tool for data analysis in Excel. We can group data by certain values in a given column and filter out rows. In pandas, we can easily filter out rows from our `DatFrame` by using Boolean logic. For this example, we would like to determine the student's name that belong to section \"A\". This is done in pandas by first creating an array of True/False values. This array corresponds to which rows met the condition. We then use the resulting Boolean array to only call rows that meet our condition." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Student NameSection
0Cecilia RichardsA
3Steve HayesA
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" + ], + "text/plain": [ + " Student Name Section\n", + "0 Cecilia Richards A\n", + "3 Steve Hayes A" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IX = sections['Section'] == 'A' # Boolean (True/False) indices or rows that met our filtering criteria\n", + "sections[IX] # use the Boolean indices to access rows" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For each section, we would like to calculate the mean score for each exam. We can create pivot tables in pandas by using either the `pivot_table` or `group_by` method. Using the `pivot_table` method, the syntax is" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Exam 2 Score Exam 1 Score\n", + "Section \n", + "A 57.5 80.0\n", + "B 78.0 64.5\n", + "C 33.0 83.0" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "student_data.groupby('Section').agg({'Exam 1 Score': 'mean', 'Exam 2 Score': 'mean'})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "In the above code, after we applied `groupby`, we then used the `agg` method and passed a Python dictionary. The keys of the dictionary are the column names to apply the aggregation and the values are the actual aggregation function. If we want to apply different or more than one aggregation function to each column, we can pass a dictionary whose values consist of lists of aggregation functions." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Exam 2 Score Exam 1 Score \n", + " max min max mean\n", + "Section \n", + "A 73 42 91 80.0\n", + "B 81 75 76 64.5\n", + "C 33 33 83 83.0" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "student_data.groupby('Section').agg({'Exam 1 Score':['max', 'mean'], 'Exam 2 Score': ['max', 'min']})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concluding Remarks\n", + "\n", + "Pandas provides the powerful `DataFrame` class, of which Excel users will recognize as resembling and behaving similarly to an Excel spreadsheet. Through the `DataFrame` class, one can perform operations equivalent to Excel's Vlookup and pivot tables. One of the barriers of learning a new tool is knowing how to perform operations one was accustomed to with the old tool. This article shows pandas' syntax, illustrating how pandas works and easing one's transition from Excel. Learning pandas is a good start in familiarizing oneself with the toolkit of a data scientist." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}