Creating dataframe from numpy array

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Creating dataframe from numpy array

Creating a pandas dataframe from numpy arrays.

On the most basic level, Pandas objects can be thought of as improved numb verses structured arrays where lines and columns are identified with rigulus instead of simple integers. As we will see in the course of this chapter, Pandas provides a series of useful tools, all and functionality on the basic data structures, but almost everything that follows will require understanding than these healthy structures ? o. Thus, before we go forward, we will introduce these three fundamental structures of pandas data: the system plot data, and index. Let's start our code sessions with Numpy Import and Standard Pandas: Numpy Import as Pandas NP Import as PD The Serious Pandas is a unidimensional matrix of indexed data. It can be created from a list or array as follows: data = pd.series ([0.25, 0.5, 0.75, 1.0]) data 0 0.25 1 0.50 2 0 , 75 3 1.00 DTPO: float64 As we can see in the Sa?da, the envelopes series both a sequence of values and a sequence of index, which can access the index values and attributes. Values are simply familiar Numpy Matrix: Matrix ([0.25, 0.5, 0.75, 1]) The index is a type object type pd.Index, which we will discuss in greater detail momentarily. RangeIndex (Start = 0, Stop = 4, Step = 1) As with a Numpy array, the data can be accessed by the associated index via the familiar python of brackets NOTE: 1 0.50 2 0.75 DTPO: Float64 As we will see, though, the Serious Pandas is much more general and flexible than the unidimensional numpy array that emulates. From what we have seen until now, it may seem that the Series object is basically interchangeable with a unidimensional numpy array. The essential difference is the presence of the inex: While the Numpy matrix has an implicitly defined integer used for access of the values, the Blessed Pandas has an explicitly defined index associated with the values. This explanatory index definition gives the sound object additional capabilities. For example, the index does not need to be an entire number, but may consist of values of any desired type. For example, if we wish, we can use strings as an index: data = pd.series ([0.25, 0.5, 0.75, 1.0], index = ['a', 'B', 'C ',' D 'd']) 0.25 B 0.50 C 0.75 d 1.00 dtpo: float64 and accessories item as expected: we can still use non-contiguous or non-sequential levels : data = pd.series ([0.25, 0.5, 0.75, 1.0], index = [2, 5, 3, 7]) data 2 0.25 5 0.50 3 0,75 7 1.00 DTPO: Float64 In this way, it is possible to think of a little pandas a bit as a specialization of a python dictionary. A dicionary is a structure that maps arbitrary keys to a set of arbitrary values, and a series is a structure that maps the keys entered to a set of typed values. This digitation is important: just as the specific type code compiled behind a Numpy matrix makes it more efficient than a Python list for certain operations, the type of s?o information ? Rie Pandas makes it much more efficient than Python dicionary for certain operations. S? ? As-dictionary Analogy can be made even lighter with the construction of a Series object directly from a python dicionary: population_dict = {'California': 38332521, 'Texas' : 26448193, 'New York': 19651127, 'Florida': 19552860, 'Illinois': 12882135} population = pd.series (population_dict) Population California 38332521 Flourida 19552860 Illinois 12882135 New York 19651127 Texas 26448193 DTPO: INT64 By pattern, a series will be created when the index is elaborated from the ordered keys. From here, the accessible article style dictionary can be performed: unlike a dictionary, however, the s?ste also supports range style operations like cutting: population ['California': 'Illinois'] California 38332521 Flourida 19552860 Illinois 12882135 DTPO: Int64 We have already seen some ways Build a Zero Serious Banda; All of them are any versions of the following: >>> pd.series (data, index = index), where index is an optional argument, and data can be one of the many entities. For example, data can be a list or Numpy Numpy In that inex case the pattern is an integer sequence: data can be a climb, which is repeated to fill the specified index: pd.series (5, index = [100, 200, 300]) 100 5 200 5 300 5 DTPO: Int64 data can be a dictionary, in which standard standard for classified dictionary keys: pd.series ({2: 'a', 1: 'B', 3: 'C'}) 1 2 B 3 C A DTPO: Object in each case, the index can be explicitly defined, if a different result is preferred: pd.series ({2: 'A', 1: 'B', 3: 'C'}, index = [3, 2]) Note that in this case, Ser? Rie is filled only with the explicitly identified keys. The next fundamental structure in pandas is the data plot. As the subject matter discussed in the previous section, the data plot can be considered as much as a generalization of a Numpy matrix, or as a specialization of a python dictionary. Let's now take a look at each of these perspectives. If a series is an analogue of a unidimensional array with flexible sites, a data plot is an analogue of a twodimensional matrix with both flexible line rows and the names of the flexible columns. Just as you can think of a two-dimensional matrix as an ordered sequence of unidimensional columns aligned, you can think of a data plot as a s?st rice sequence aligned objects. Here, by "aligned" we mean they share the same inex. To demonstrate this, we will first build a new series listing the area of each of the five states discussed in the previous section: area_dict = {'California': 423967, 'Texas': 695662,' New York ': 141297,' Florida ': 170312,' Illinois': 149995} area = pd.series area (area_dict) California 423.967 Flourida 170.312 Illinois 149995 New York 141297 Texas 695.662 DTPO: INT64 Now that we have this along with the population Series of Before, we can use a dictionary for the construction of a simple two-dimensional object containing this information: states = pd.dataframe ({'population': population , 'Area': area}) states such as the subject of day, the data plot has an attribute that gives access to the second markers: Index (['California', 'Florida' , 'Illinois', 'New York', 'Texas'], DTPO = 'Object') In addition, the data frame has an attribute columns, which is an index object holding the bookmarks column: in the ndice ( ['Area', 'population'], dture = 'object') thus, The data plot can be thought of as a generalization of a two-dimensional numpy matrix, where both queues and columns have a widespread intex to access the data. In the same way, we can also think of a data plot as a specialization of a dictionary. Where a dictionary maps a key to a value, a data frame maps a column name to a column data. For example, asking for the 'area' attribute returns the Series object containing the areas we saw earlier: California 423,967 Flourida 170.312 Illinois 149995 New York 141297 Texas 695662 Name: Aear, DTPO: Int64 Observe the potential point of Confusion here: In two matrix -Dimesnional Numpy, data [0] will return the first row. For a data frame, data ['col0'] returns the first column. Because of this, it is probably better to think about datoframes as generalized dicionary rather than generalized matrices, although both ways to look at the situation can be useful. Let's explore more flexible means of indexing Dataframes in data index and selection. The pandas data plot can be constructed in a variety of ways. Here we will give several examples. A data plot is a collection of sound objects, and a single column data plot can be built from a single s?st rie: pd.dataframe (population, columns = ['Population']) Any list of dictionaries can be made in a data plot. We will use a simple list of understanding to create some data: data = [{'a': i, 'b': 2 * i} for i in range (3)] pd.dataframe Even if some keys in the dictionary are absent, pandas will fill them with NAN (ie, "not a number") Values: pd.dataframe ([{'to': 1, 'B': 2}, {'B': 3, 'C': 4}) As we have seen before, a data plot can be constructed from a daytime dictionary objects as well as: PD .Dataframe ({'population': population, 'area': area}) Given a Data matrix, which can create a data frame with the specified column and index names. If omitted, a whole index will be used for each: pd.dataframe (np.random.rand (3, 2), columns = ['foo', 'bar'], index = ['A', 'B ',' c ') a = np.zeros (3, dtipo = [(' a ',' I8 '), (' B ',' F8 '))) an array ([(0, 0.0) , (0, 0.0), (0, 0.0)], DTIPO = [('A', ' 1245 RAISE TYPEERROR ("In the ndex does not support mutable operations") 1246 1247 DEF __GETITEM __ (SELF, KEY): TYPEERROR: Ndex does not support mutable operations ?

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