Pandas display


  • How to Show All Columns of a Pandas DataFrame
  • Display the Pandas DataFrame in table style
  • The Pandas DataFrame – loading, editing, and viewing data in Python
  • How to Pretty Print Dataframe in Pandas- Detailed Guide
  • How to Access a Column in a DataFrame
  • How to Show All Columns of a Pandas DataFrame

    Wednesday Visualizing time series data With pandas and matplotlib, we can easily visualize our time series data. However, with so many data points, the line plot is crowded and hard to read.

    Electricity consumption appears to split into two clusters — one with oscillations centered roughly around GWh, and another with fewer and more scattered data points, centered roughly around GWh. We might guess that these clusters correspond with weekdays and weekends, and we will investigate this further shortly. Solar power production is highest in summer, when sunlight is most abundant, and lowest in winter.

    Wind power production is highest in winter, presumably due to stronger winds and more frequent storms, and lowest in summer. There appears to be a strong increasing trend in wind power production over the years. All three time series clearly exhibit periodicity—often referred to as seasonality in time series analysis—in which a pattern repeats again and again at regular time intervals. The Consumption, Solar, and Wind time series oscillate between high and low values on a yearly time scale, corresponding with the seasonal changes in weather over the year.

    However, seasonality in general does not have to correspond with the meteorological seasons. For example, retail sales data often exhibits yearly seasonality with increased sales in November and December, leading up to the holidays. Seasonality can also occur on other time scales. Another interesting feature that becomes apparent at this level of granularity is the drastic decrease in electricity consumption in early January and late December, during the holidays.

    Customizing time series plots To better visualize the weekly seasonality in electricity consumption in the plot above, it would be nice to have vertical gridlines on a weekly time scale instead of on the first day of each month.

    We can customize our plot with matplotlib. Then we use mdates. WeekdayLocator and mdates. We also use mdates. DateFormatter to improve the formatting of the tick labels, using the format codes we saw earlier. We saw this in the time series for the year , and the box plot confirms that this is consistent pattern throughout the years.

    The low outliers on weekdays are presumably during holidays. This section has provided a brief introduction to time series seasonality. As we will see later, applying a rolling window to the data can also help to visualize seasonality on different time scales.

    Other techniques for analyzing seasonality include autocorrelation plots , which plot the correlation coefficients of the time series with itself at different time lags. Time series with strong seasonality can often be well represented with models that decompose the signal into seasonality and a long-term trend, and these models can be used to forecast future values of the time series.

    A simple example of such a model is classical seasonal decomposition , as demonstrated in this tutorial. Frequencies When the data points of a time series are uniformly spaced in time e. Available frequencies in pandas include hourly 'H' , calendar daily 'D' , business daily 'B' , weekly 'W' , monthly 'M' , quarterly 'Q' , annual 'A' , and many others. Frequencies can also be specified as multiples of any of the base frequencies, for example '5D' for every five days.

    This makes sense, since the index was created from a sequence of dates in our CSV file, without explicitly specifying any frequency for the time series.

    Display the Pandas DataFrame in table style

    Pandas development started in with main developer Wes McKinney and the library has become a standard for data analysis and management using Python. Pandas fluency is essential for any Python-based data professional, people interested in trying a Kaggle challenge , or anyone seeking to automate a data process. The aim of this post is to help beginners get to grips with the basic data format for Pandas — the DataFrame.

    We will examine basic methods for creating data frames, what a DataFrame actually is, renaming and deleting data frame columns and rows, and where to go next to further your skills. The topics in this post will enable you hopefully to: Load your data from a file into a Python Pandas DataFrame , Examine the basic statistics of the data, Change some values, Finally output the result to a new file.

    What is a Python Pandas DataFrame? In plain terms, think of a DataFrame as a table of data, i. Each row represents a sample of data, Each column contains a different variable that describes the samples rows. The data in every column is usually the same type of data — e.

    Usually, unlike an excel data set, DataFrames avoid having missing values, and there are no gaps and empty values between rows or columns. By way of example, the following data sets that would fit well in a Pandas DataFrame: In a school system DataFrame — each row could represent a single student in the school, and columns may represent the students name string , age number , date of birth date , and address string. In an economics DataFrame, each row may represent a single city or geographical area, and columns might include the the name of area string , the population number , the average age of the population number , the number of households number , the number of schools in each area number etc.

    In a shop or e-commerce system DataFrame, each row in a DataFrame may be used to represent a customer, where there are columns for the number of items purchased number , the date of original registration date , and the credit card number string. Manually entering data The start of every data science project will include getting useful data into an analysis environment, in this case Python.

    Using Python dictionaries and lists to create DataFrames only works for small datasets that you can type out manually.

    There are other ways to format manually entered data which you can check out here. However, for simplicity, sometimes extracting data directly to CSV and using that is preferable. You can download the CSV file from Kaggle, or directly from here. The data is nicely formatted, and you can open it in Excel at first to get a preview: The sample data for this post consists of food global production information spanning to The sample data contains 21, rows of data, with each row corresponding to a food source from a specific country.

    Some installation instructions are here. Printing is a convenient way to preview your loaded data, you can confirm that column names were imported correctly, that the data formats are as expected, and if there are missing values anywhere. In a Jupyter notebook, simply typing the name of a data frame will result in a neatly formatted outputs.

    This is an excellent way to preview data, however notes that, by default, only rows will print, and 20 columns. You can see the full set of options available in the official Pandas options and settings documentation. DataFrame rows and columns with. Get the shape of your DataFrame — the number of rows and columns using.

    Our food production data contains 21, rows, each with 63 columns as seen by the output of. We have two dimensions — i. If your data had only one column, ndim would return 1. Data sets with more than two dimensions in Pandas used to be called Panels, but these formats have been deprecated. The opposite is DataFrame. Pass in a number and Pandas will print out the specified number of rows as shown in the example below.

    Head and Tail need to be core parts of your go-to Python Pandas functions for investigating your datasets. The first 5 rows of a DataFrame are shown by head , the final 5 rows by tail. For other numbers of rows — simply specify how many you want! In our example here, you can see a subset of the columns in the data since there are more than 20 columns overall. Data types dtypes of columns Many DataFrames have mixed data types, that is, some columns are numbers, some are strings, and some are dates etc.

    Internally, CSV files do not contain information on what data types are contained in each column; all of the data is just characters. Pandas infers the data types when loading the data, e. See the data types of each column in your dataframe using the. In some cases, the automated inferring of data types can give unexpected results. This behaviour is expected, and can be ignored.

    To change the datatype of a specific column, use the. For numeric columns, describe returns basic statistics : the value count, mean, standard deviation, minimum, maximum, and 25th, 50th, and 75th quantiles for the data in a column. Note the differences between columns with numeric datatypes, and columns of strings and characters.

    Note that if describe is called on the entire DataFrame, statistics only for the columns with numeric datatypes are returned, and in DataFrame format. Describing a full dataframe gives summary statistics for the numeric columns only, and the return format is another DataFrame. Selecting and Manipulating Data The data selection methods for Pandas are very flexible. For detailed information and to master selection, be sure to read that post. For this example, we will look at the basic method for column and row selection.

    Selecting columns There are three main methods of selecting columns in pandas: using a dot notation, e. The square brackets with column name method is the least error prone in my opinion. When a column is selected using any of these methodologies, a pandas.

    Series is the resulting datatype. A pandas series is a one-dimensional set of data. Series summary operations. We are selecting the column "Y", and performing various calculations. For selection of multiple columns, the syntax is: square-brace selection with a list of column names, e. The basic methods to get your heads around are: numeric row selection using the iloc selector, e.

    Note that you can combine the selection methods for columns and rows in many ways to achieve the selection of your dreams. Summary of iloc and loc methods discussed in the iloc and loc selection blog post. The drop function returns a new DataFrame, with the columns removed. The drop function in Pandas be used to delete rows from a DataFrame, with the axis set to 0.

    As before, the inplace parameter can be used to alter DataFrames without reassignment. The rename function is easy to use, and quite flexible. Functions are applied to every column name. Data output in Pandas is as simple as loading data. For the excel output to work, you may need to install the "xlsxwriter" package. You will also need import matplotlib. A huge amount of functionality is provided by the. Create a histogram showing the distribution of latitude values in the dataset.

    Create a bar plot of the top food producers with a combination of data selection, data grouping, and finally plotting using the Pandas DataFrame plot command.

    All of this could be produced in one line, but is separated here for clarity. With enough interest, plotting and data visualisation with Pandas is the target of a future blog post — let me know in the comments below!

    For more information on visualisation with Pandas, make sure you review:.

    The Pandas DataFrame – loading, editing, and viewing data in Python

    The data is nicely formatted, and you can open it in Excel at first to get a preview: The sample data for this post consists of food global production information spanning to The sample data contains 21, rows of data, with each row corresponding to a food source from a specific country.

    Some installation instructions are here. Printing is a convenient way to preview your loaded data, you can confirm that column names were imported correctly, that the data formats are as expected, and if there are missing values anywhere.

    In a Jupyter notebook, simply typing the name of a data frame will result in a neatly formatted outputs. This is an excellent way to preview data, however notes that, by default, only rows will print, and 20 columns. You can see the full set of options available in the official Pandas options and settings documentation. DataFrame rows and columns with. Get the shape of your DataFrame — the number of rows and columns using.

    How to Pretty Print Dataframe in Pandas- Detailed Guide

    Our food production data contains 21, rows, each with 63 columns as seen by the output of. We have two dimensions — i. If your data had only one column, ndim would return 1.

    Data sets with more than two dimensions in Pandas used to be called Panels, but these formats have been deprecated. The opposite is DataFrame. Pass in a number and Pandas will print out the specified number of rows as shown in the example below. Head and Tail need to be core parts of your go-to Python Pandas functions for investigating your datasets. The first 5 rows of a DataFrame are shown by headthe final 5 rows by tail. For other numbers of rows — simply specify how many you want!

    In our example here, you can see a subset of the columns in the data since there are more than 20 columns overall. Data types dtypes of columns Many DataFrames have mixed data types, that is, some columns are numbers, some are strings, and some are dates etc.

    Internally, CSV files do not contain information on what data types are contained in each column; all of the data is just characters. Pandas infers the data types when loading the data, e. See the data types of each column in your dataframe using the. In some cases, the automated inferring of data types can give unexpected results.

    This behaviour is expected, and can be ignored. To change the datatype of a specific column, use the. For numeric columns, describe returns basic statistics : the value count, mean, standard deviation, minimum, maximum, and 25th, 50th, and 75th quantiles for the data in a column. Note the differences between columns with numeric datatypes, and columns of strings and characters.

    Note that if describe is called on the entire DataFrame, statistics only for the columns with numeric datatypes are returned, and in DataFrame format. Describing a full dataframe gives summary statistics for the numeric columns only, and the return format is another DataFrame.

    Selecting and Manipulating Data The data selection methods for Pandas are very flexible. For detailed information and to master selection, be sure to read that post. For this example, we will look at the basic method for column and row selection.

    Selecting columns There are three main methods of selecting columns in pandas: using a dot notation, e. The square brackets with column name method is the least error prone in my opinion.

    When a column is selected using any of these methodologies, a pandas. Series is the resulting datatype. A pandas series is a one-dimensional set of data. All three time series clearly exhibit periodicity—often referred to as seasonality in time series analysis—in which a pattern repeats again and again at regular time intervals.

    How to Access a Column in a DataFrame

    The Consumption, Solar, and Wind time series oscillate between high and low values on a yearly time scale, corresponding with the seasonal changes in weather over the year.

    However, seasonality in general does not have to correspond with the meteorological seasons. For example, retail sales data often exhibits yearly seasonality with increased sales in November and December, leading up to the holidays. Seasonality can also occur on other time scales. Another interesting feature that becomes apparent at this level of granularity is the drastic decrease in electricity consumption in early January and late December, during the holidays.

    Customizing time series plots To better visualize the weekly seasonality in electricity consumption in the plot above, it would be nice to have vertical gridlines on a weekly time scale instead of on the first day of each month. We can customize our plot with matplotlib. Then we use mdates. WeekdayLocator and mdates. We also use mdates. DateFormatter to improve the formatting of the tick labels, using the format codes we saw earlier. We saw this in the time series for the yearand the box plot confirms that this is consistent pattern throughout the years.

    The low outliers on weekdays are presumably during holidays. This section has provided a brief introduction to time series seasonality.


    Pandas display