## convert python map objects to array or dataframe

python map() function

python map object attributes

convert map to array python

python map list to list

python map multiple functions

mapping object python

convert map to tuple python

How can we convert map objects(derived from ndarray objects) to a dataframe or array object in python.

I have a normally distributed data with size 10*10 called a. There is one more data containing 0 and 1 of size 10*10 called b. I want to add a to b if b is not zero else return b.

I am doing it through map. I am able to create the map object called c but can't see the content of it. Can someone please help.

a=numpy.random.normal(loc=0.0,scale=0.001,size=(10,10)) b = np.random.randint(2, size=a.shape) c=map(lambda x,y : y+x if y!=0 else x, a,b) a=[[.24,.03,.87], [.45,.67,.34], [.54,.32,.12]] b=[[0,1,0], [1,0,0], [1,0,1]]

then c should be as shown below.

c=[[0,1.03,.87], [1.45,0,0], [1.54,0,1.12] ]

np.multiply(a,b) + b

should do it

Here is the output

array([[0. , 1.03, 0. ], [1.45, 0. , 0. ], [1.54, 0. , 1.12]])

**Creating a DataFrame from Objects,** DataFrame is a constructor # create a dictionary ex_dic = { 'id': [100, 101, 102], create 4x2 random array # array is a list of lists arr = np.random.rand(4, 2) arr. Creating a DataFrame from objects in pandas Creating a DataFrame from objects This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code.

You are greatly hampering your code by using map, which is slow and usually not advised for simple mappings even in regular Python - but especially since pure Numpy works so much faster than Python I would not use it. This is all without mentioning your code will not even compile (try `list(c)`

to see what happens).

Remember that in Numpy you can basically do all your element wise stuff quite easily. Generate the indices you want to work on, and do that explicitly:

indices = b==0 c=a+b c[indices]=0

Here basically I just sum the matrices element wise, and then 0 out the indices I do not want. @RobS answer is a cooler way of doing this.

**Understand map() function to manipulate pandas Series,** Learn the fundamentals of using the map() function to convert the data to most popular Python libraries for data science research, the pandas library In pandas, a Series is a one-dimensional array-like object containing a Examples of Converting a List to DataFrame in Python Example 1: Convert a List. Let’s say that you have the following list that contains the names of 5 people: People_List = ['Jon','Mark','Maria','Jill','Jack'] You can then apply the following syntax in order to convert the list of names to pandas DataFrame:

Since, `a`

and `b`

are numpy arrays, there is a numpy function especially for this use case as `np.where`

(documentation).

If a and b are as follows,

a=np.array([[.24,.03,.87], [.45,.67,.34], [.54,.32,.12]]) b=np.array([[0,1,0], [1,0,0], [1,0,1]])

Then the output of the following line,

np.where(b!=0, a+b, b)

will be,

[[0. 1.03 0. ] [1.45 0. 0. ] [1.54 0. 1.12]]

**pandas.Series.map,** Map values of Series using input correspondence (a dict, Series, or function). values in Series that are not in the dictionary (as keys) are converted to NaN . 1.0 1 this is a string 2.0 2 this is a string 3.0 3 this is a string nan dtype: object. You can convert a Pandas DataFrame to Numpy Array to perform some high-level mathematical functions supported by Numpy package. To convert Pandas DataFrame to Numpy Array, use the function DataFrame.to_numpy() . to_numpy() is applied on this DataFrame and the method returns object of type Numpy ndarray.

**pandas.Series.map,** Values that are not found in the dict are converted to NaN , unless the dict has a a {}'.format) 0 I am a cat 1 I am a dog 2 I am a nan 3 I am a rabbit dtype: object. To get the link to csv file, click on nba.csv. Example 1: Changing the DataFrame into numpy array by using a method DataFrame.to_numpy().Always remember that when dealing with lot of data you should clean the data first to get the high accuracy.

**Introducing Pandas Objects,** A Pandas Series is a one-dimensional array of indexed data. can be made even more clear by constructing a Series object directly from a Python dictionary:. Questions: I have manipulated some data using pandas and now I want to carry out a batch save back to the database. This requires me to convert the dataframe into an array of tuples, with each tuple corresponding to a “row” of the dataframe.

**Python,** It is generally the most commonly used pandas object. Let's discuss how to create Pandas dataframe using dictionary of ndarray (or lists). Let's try to understand it If index is passed then the length index should be equal to the length of arrays. first_page Python | Convert flattened dictionary into nested dictionary. Next. Convert DataFrame, Series to ndarray: values. Both pandas.DataFrame and pandas.Series have valiues attribute that returns NumPy array numpy.ndarray.After pandas 0.24.0, it is recommended to use the to_numpy() method introduced at the end of this post.

##### Comments

- Why is there 0.87 in the first row of c?
- Good answer. Note you can use
`a*b+b`

for an even niftier line, or`(a+1)*b`

. The`*`

operator is properly overloaded to work element-wise, jsut like`+`

. - Thanks! Unfortunately either don't work though as a and b turn out to be lists and not ndarrays
- OP explicitly shows their input is a Numpy array, I think it is safe to assume they are.
- Great Thank you Pranav.