A Data frame is a two-dimensional data structure, i. For the row labels, the Index to be used for the resulting frame is Optional Default np.

For column labels, the optional default syntax is - np. This is only true if no index is passed. In the subsequent sections of this chapter, we will see how to create a DataFrame using these inputs. All the ndarrays must be of same length.

If index is passed, then the length of the index should equal to the length of the arrays. If no index is passed, then by default, index will be range nwhere n is the array length.

They are the default index assigned to each using the function range n. List of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names.

The following example shows how to create a DataFrame by passing a list of dictionaries and the row indices. The following example shows how to create a DataFrame with a list of dictionaries, row indices, and column indices.

Dictionary of Series can be passed to form a DataFrame. The resultant index is the union of all the series indexes passed. We will now understand row selection, addition and deletion through examples. Let us begin with the concept of selection. The result is a series with labels as column names of the DataFrame.

And, the Name of the series is the label with which it is retrieved. Add new rows to a DataFrame using the append function.When doing data analysis, it is important to make sure you are using the correct data types; otherwise you may get unexpected results or errors. Despite how well pandas works, at some point in your data analysis processes, you will likely need to explicitly convert data from one type to another.

A data type is essentially an internal construct that a programming language uses to understand how to store and manipulate data. A possible confusing point about pandas data types is that there is some overlap between pandas, python and numpy.

For the most part, there is no need to worry about determining if you should try to explicitly force the pandas type to a corresponding to NumPy type.

Most of the time, using pandas default int64 and float64 types will work. The category and timedelta types are better served in an article of their own if there is interest.

One other item I want to highlight is that the object data type can actually contain multiple different types. For instance, the a column could include integers, floats and strings which collectively are labeled as an object.

Therefore, you may need some additional techniques to handle mixed data types in object columns. I will use a very simple CSV file to illustrate a couple of common errors you might see in pandas if the data type is not correct. Upon first glance, the data looks ok so we could try doing some operations to analyze the data.

This does not look right. We would like to get totals added together but pandas is just concatenating the two values together to create one long string. A clue to the problem is the line that says dtype: object. If we want to see what all the data types are in a dataframe, use df. The simplest way to convert a pandas column of data to a different type is to use astype. This all looks good and seems pretty simple. In each of the cases, the data included values that could not be interpreted as numbers.

In the sales columns, the data includes a currency symbol as well as a comma in each value. We should give it one more try on the Active column. At first glance, this looks ok but upon closer inspection, there is a big problem. If the data has non-numeric characters or is not homogeneous, then astype will not be a good choice for type conversion.Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages.

Pandas is one of those packages and makes importing and analyzing data much easier. Pandas astype is the one of the most important methods. It is used to change data type of a series. When data frame is made from a csv file, the columns are imported and data type is set automatically which many times is not what it actually should have.

For example, a salary column could be imported as string but to do operations we have to convert it into float. Parameters: dtype: Data type to convert the series into. For example dict to string. To download the data set used in following example, click here.

In the following examples, the data frame used contains data of some NBA players. The image of data frame before any operations is attached below. Example: In this example, the data frame is imported and. After that some columns are converted using. Output: As shown in the output image, the data types of columns were converted accordingly. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.

### Python | Pandas Series.astype() to convert Data type of series

See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide. Python Pandas Series. Syntax: DataFrame. Recommended Posts: Python Pandas Series. Check out this Author's contributed articles. Load Comments.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Alternatively, I want to know, if there are any datatype apart from iii and iii in the list above that pandas does not make it's dtype an object? Pandas mostly uses NumPy arrays and dtypes for each Series a dataframe is a collection of Series, each which can have its own dtype.

NumPy's documentation further explains dtypedata typesand data type objects. In addition, the answer provided by lcameron05 provides an excellent description of the numpy dtypes. Furthermore, the pandas docs on dtypes have a lot of additional information. The main types stored in pandas objects are float, int, bool, datetime64[ns], timedelta[ns], and object. In addition these dtypes have item sizes, e.

The following will all result in int64 dtypes. Numpy, however will choose platform-dependent types when creating arrays. The following WILL result in int32 on bit platform. One of the major changes to version 1. NA to represent scalar missing values rather than the previous values of np.

NaT or Nonedepending on usage. Pandas extends NumPy's type system and also allows users to write their on extension types. The following lists all of pandas extension types. Data type: DatetimeTZDtype. Scalar: Timestamp. Array: arrays.

Data type: CategoricalDtype. Array: Categorical. Data type: PeriodDtype. Scalar: Period.The format of individual columns and rows will impact analysis performed on a dataset read into python. For example, you can't perform mathematical calculations on a string character formatted data. This might seem obvious, however sometimes numeric values are read into python as strings.

In this situation, when you then try to perform calculations on the string-formatted numeric data, you get an error. In this lesson we will review ways to explore and better understand the structure and format of our data. How information is stored in a DataFrame or a python object affects what we can do with it and the outputs of calculations as well.

There are two main types of data that we're explore in this lesson: numeric and character types. Numeric data types include integers and floats. A floating point known as a float number has decimal points even if that decimal point value is 0. For example: 1. If we have a column that contains both integers and floating point numbers, Pandas will assign the entire column to the float data type so the decimal points are not lost.

An integer will never have a decimal point. Thus 1. You will often see the data type Int64 in python which stands for 64 bit integer. The 64 simply refers to the memory allocated to store data in each cell which effectively relates to how many digits in can store in each "cell".

Allocating space ahead of time allows computers to optimize storage and processing efficiency. For example, a string might be a word, a sentence, or several sentences. A Pandas object might also be a plot name like 'plot1'. A string can also contain or consist of numbers. For instance, '' could be stored as a string. As could ' However strings that contain numbers can not be used for mathematical operations! Pandas and base Python use slightly different names for data types.

More on this is in the table below:. Now that we're armed with a basic understanding of numeric and character data types, let's explore the format of our survey data. We'll be working with the same surveys. Next, let's look at the structure of our surveys data. A type 'O' just stands for "object" which in Pandas' world is a string characters. The type int64 tells us that python is storing each value within this column as a 64 bit integer.

We can use the dat. Note that most of the columns in our Survey data are of type int This means that they are 64 bit integers. But the wgt column is a floating point value which means it contains decimals.

The species and sex columns are objects which means they contain strings. So we've learned that computers store numbers in one of two ways: as integers or as floating-point numbers or floats.Pandas has got to be one of my most favourite librariesâ€¦ Ever.

## Pandas Visualization â€“ Plot 7 Types of Charts in Pandas in just 7 min.

Pandas allows us to deal with data in a way that us humans can understand it; with labelled columns and indexes. It allows us to effortlessly import data from files such as csvs, allows us to quickly apply complex transformations and filters to our data and much more.

Along with Numpy and Matplotlib I feel it helps create a really strong base for data exploration and analysis in Python. Scipy which will be covered in the next postis of course a major component and another absolutely fantastic library, but I feel these three are the real pillars of scientific Python. This is the standard way to import Pandas. A series is a one-dimensional data type where each element is labelled.

**Pandas Tutorials # 5 : How to handle Categorical data attributes in Pandas**

If you have read the post in this series on NumPyyou can think of it as a numpy array with labelled elements. Labels can be numeric or strings. A dataframe is a two-dimensional, tabular data structure.

The Pandas dataframe can store many different data types and each axis is labelled. You can think of it as sort of like a dictionary of series. Before we can start wrangling, exploring and analysing, we first need data to wrangle, explore and analyse.

Thanks to Pandas this is very easy, more so than NumPy. Here I encourage you to find your own dataset, one that interests you and play around with that. If you search for example UK government data or US government datait will be one of the first results. Kaggle is another great source. Here we get data from a csv file and store it in a dataframe. The header keyword argument tells Pandas if and where the column names of your data are. If there are no column names you can set it to None.

### Python | Pandas DataFrame.dtypes

Pandas is pretty clever so this can often be omitted. Now we have our data in Pandas, we probably want to take a quick look at it and know some basic information about it to give us some direction before we really probe into it.

As with head all we do is call tail and pass it the number of rows we want to retrieve. It gives you the rows in the order they are in in the dataframe.Changed in version 1. NA as the missing value rather than numpy. In Working with missing datawe saw that pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point.

In some cases, this may not matter much. But if your integer column is, say, an identifier, casting to float can be problematic. Some integers cannot even be represented as floating point numbers. Pandas can represent integer data with possibly missing values using arrays. This is an extension types implemented within pandas. All NA-like values are replaced with pandas. You can also pass the list-like object to the Series constructor with the dtype. Currently pandas.

Series use different rules for dtype inference. For backwards-compatibility, Series infers these as either integer or float dtype.

In the future, we may provide an option for Series to infer a nullable-integer dtype. Operations involving an integer array will behave similar to NumPy arrays. Missing values will be propagated, and the data will be coerced to another dtype if needed. These dtypes can operate as part of of DataFrame. IntegerArray uses pandas.

NA as its scalar missing value. Home What's New in 1. On this page.

Note IntegerArray is currently experimental. Its API or implementation may change without warning. In [3]: pd. In [4]: pd. In [5]: pd. Warning Currently pandas. In [8]: pd. Series [ 1None ] Out[8]: 0 1. Series [ 12 ] Out[9]: 0 1 1 2 dtype: int In [10]: pd. In [21]: pd. In [23]: df. Categorical data Nullable Boolean Data Type.