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The book effectively guides you through installing the library, creating complex data structures, deriving data from the internet, and finally visualizing your results with the help of plots and graphs.

The learning curve is not steep, and thus, it attracts a lot of beginners. Simple, accurate and versatile are the best terms to describe this book.

Hailed as one of the best books to learn Pandas , this book is comparatively lightweight when compared to the other books on the list. It is not a data-heavy book but provides the user with a glimpse of the real power vested by the Pandas library.

Meaning that it is perfect for people who want to get the basics of Pandas clear as soon as possible without wasting time beating about the bush.

The book has thorough coverage of Pandas DataFrame and the various activities one can perform with the help of DataFrames.

This book can be termed as the perfect reference book. With more than 95 recipes to showcase the power of the library, readers will be able to analyze data like never before.

The book empowers you with the knowledge of solving huge computing problems, create complex data structures, get more meaning out of your data, and much more.

Worry not, this pandas book will solve everything. Why waste money on another book for python, when you can have the knowledge required to code in python and Pandas on the same book?

This book is aimed at absolute beginners who have zero programming knowledge. It picks you up, guides you and channels your mind to know exactly what needs to start Data Analysis with Python and Pandas.

As the title of the book states, it is for everyone. This makes it a definite pick to be one of the best books to learn Pandas.

Setting that aside, this book is very comprehensive and provides its readers with the experience of not only Pandas but also NumPy. While imparting the basic knowledge of Pandas, it also gives readers the knowledge of creating hierarchical indexes with DataFrames and plot that too.

From the above Python Pandas books, now you can select, which one is best suitable for you. However when combined with comparison operators one has to remember to wrap the comparison in parenthesis because the bitwise operators have a higher precedence than the comparison operators :.

If these don't contain booleans then the operations will give different results. I'll include examples using NumPy arrays but the results will be similar for the pandas data structures:.

The others work on these data structures and plain Python objects and work element-wise. Learn more. Logical operators for boolean indexing in Pandas Ask Question.

Asked 6 years, 5 months ago. Active 1 year, 1 month ago. Viewed k times. I'm working with boolean index in Pandas.

Steve It therefore does not make sense to use the logical operator in this situation. In Python and! Related: Truth value of a Series is ambiguous.

Active Oldest Votes. NumPy arrays of length greater than 1 and Pandas objects such as Series do not have a boolean value -- in other words, they raise ValueError: The truth value of an array is ambiguous.

Indominus: The Python language itself requires that the expression x and y triggers the evaluation of bool x and bool y. Python "first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Miller Jul 18 '17 at What is " " for? Consider the following setup: np. DataFrame np. So, don't make that mistake!

If the individual masks are built up using functions instead of conditional operators, you will no longer need to group by parens to specify evaluation order: df['A'].

Alternatively, this operation can be specified with df[df['A']. Series [True, True, False] If you need to invert every boolean value so that the end result is [False, False, True] , then you can use any of the methods below.

May I ask why? Isn't numpy. Why add the burden of doing it bitwise for each pair of elements? I cannot find what you're referring to.

But to me, that documentation is more of a "tutorial", and in contrast, I feel these API references are closer to the source of truth: numpy.

Logical operators for boolean indexing in Pandas It's important to realize that you cannot use any of the Python logical operators and , or or not on pandas.

NumPys logical functions However NumPy provides element-wise operating equivalents to these operators as functions that can be used on numpy.

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If the individual masks are built up using functions instead of conditional operators, you will no longer need to group by parens to specify evaluation order:.

See the section on Flexible Comparisons. To summarise, we have. Another option for avoiding parentheses is to use DataFrame.

I have extensively documented query and eval in Dynamic Expression Evaluation in pandas using pd. Internally calls Series.

Generalizing: np. This is powerful, because it lets you build on top of this with more complex logic for example, dynamically generating masks in a list comprehension and adding all of them :.

This is a very , very common beginner's mistake, and must be explained very thoroughly. If you haven't yet, please also read the section on Logical AND above, all caveats apply here.

If you need to invert every boolean value so that the end result is [False, False, True] , then you can use any of the methods below.

Note, np. It's important to realize that you cannot use any of the Python logical operators and , or or not on pandas. Series or pandas.

DataFrame s similarly you cannot use them on numpy. The reason why you cannot use those is because they implicitly call bool on their operands which throws an Exception because these data structures decided that the boolean of an array is ambiguous:.

I did cover this more extensively in my answer to the "Truth value of a Series is ambiguous. Use a.

However NumPy provides element-wise operating equivalents to these operators as functions that can be used on numpy.

Series , pandas. DataFrame , or any other conforming numpy. So, essentially, one should use assuming df1 and df2 are pandas DataFrames :.

However in case you have boolean NumPy array, pandas Series, or pandas DataFrames you could also use the element-wise bitwise functions for booleans they are - or at least should be - indistinguishable from the logical functions :.

Typically the operators are used. However when combined with comparison operators one has to remember to wrap the comparison in parenthesis because the bitwise operators have a higher precedence than the comparison operators :.

If these don't contain booleans then the operations will give different results. I'll include examples using NumPy arrays but the results will be similar for the pandas data structures:.

The others work on these data structures and plain Python objects and work element-wise. Learn more.

Logical operators for boolean indexing in Pandas Ask Question. Asked 6 years, 5 months ago. Active 1 year, 1 month ago. Viewed k times.

I'm working with boolean index in Pandas. Steve It therefore does not make sense to use the logical operator in this situation.

In Python and! Related: Truth value of a Series is ambiguous. Active Oldest Votes. NumPy arrays of length greater than 1 and Pandas objects such as Series do not have a boolean value -- in other words, they raise ValueError: The truth value of an array is ambiguous.

Indominus: The Python language itself requires that the expression x and y triggers the evaluation of bool x and bool y.

Python "first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Miller Jul 18 '17 at What is " " for? Consider the following setup: np. DataFrame np. So, don't make that mistake! If the individual masks are built up using functions instead of conditional operators, you will no longer need to group by parens to specify evaluation order: df['A'].

Alternatively, this operation can be specified with df[df['A']. Series [True, True, False] If you need to invert every boolean value so that the end result is [False, False, True] , then you can use any of the methods below.

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