datatypes en pandas

https://pbpython.com/pandas_dtypes.html

Pandas Data Types

A data type is essentially an internal construct that a programming language uses to understand how to store and manipulate data. For instance, a program needs to understand that you can add two numbers together like 5 + 10 to get 15. Or, if you have two strings such as “cat” and “hat” you could concatenate (add) them together to get “cathat.”
A possible confusing point about pandas data types is that there is some overlap between pandas, python and numpy. This table summarizes the key points:

Pandas dtype mapping

Pandas dtype	Python type	NumPy type	Usage
object	str	string_, unicode_	Text
int64	int	int_, int8, int16, int32, int64, uint8, uint16, uint32, uint64	Integer numbers
float64	float	float_, float16, float32, float64	Floating point numbers
bool	bool	bool_	True/False values
datetime64	NA	datetime64[ns]	Date and time values
timedelta[ns]	NA	NA	Differences between two datetimes
category	NA	NA	Finite list of text values

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 only reason I included in this table is that sometimes you may see the numpy types pop up on-line or in your own analysis.

GEWOONTE: Check datatypes bij inlezen DATAFRAME

Check bij het inlezen van data in een dataframe altijd eerst de datatypes

df.dtypes

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 10 columns):
Customer Number    5 non-null float64
Customer Name      5 non-null object
2016               5 non-null object
2017               5 non-null object
Percent Growth     5 non-null object
Jan Units          5 non-null object
Month              5 non-null int64
Day                5 non-null int64
Year               5 non-null int64
Active             5 non-null object
dtypes: float64(1), int64(3), object(6)
memory usage: 480.0+ bytes

After looking at the automatically assigned data types, there are several concerns:

• The Customer Number is a float64 but it should be an int64
• The 2016 and 2017 columns are stored as objects, not numerical values such as a float64 or int64
• Percent Growth and Jan Units are also stored as objects not numerical values
• We have Month , Day and Year columns that should be converted to datetime64
• The Active column should be a boolean

Until we clean up these data types, it is going to be very difficult to do much additional analysis on this data.

Schoon de datatypes op

In order to convert data types in pandas, there are three basic options:

• Use astype() to force an appropriate dtype
• Create a custom function to convert the data
• Use pandas functions such as to_numeric() or to_datetime()

 ASTYPE() functie

The simplest way to convert a pandas column of data to a different type is to use astype() .

van float64 naar int64

The simplest way to convert a pandas column of data to a different type is to use astype() . For instance, to convert the Customer Number to an integer we can call it like this:

df['Customer Number'].astype('int')

van object naar float64

Lijkt makkelijk maar er zit hier een addertje onder het gras

 Let’s try to do the same thing to our 2016 column and convert it to a floating point number:

df['2016'].astype('float') 

ValueError       Traceback (most recent call last)
<ipython-input-45-999869d577b0> in <module>()
----> 1 df['2016'].astype('float')

[lots more code here]

ValueError: could not convert string to float: '$15,000.00'

In a similar manner, we can try to conver the Jan Units column to an integer:

df['Jan Units'].astype('int')

ValueError         Traceback (most recent call last)

<ipython-input-44-31333711e4a4> in <module>()
----> 1 df['Jan Units'].astype('int')

[lots more code here]


ValueError: invalid literal for int() with base 10: 'Closed'

===>
Both of these return ValueError exceptions which mean that the conversions did not work.
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. In the Jan Units columnm the last value is “Closed” which is not a number; so we get the exception.
So far it’s not looking so good for astype() as a tool. We should give it one more try on the Active column.

df['Active'].astype('bool')

0    True
1    True
2    True
3    True
4    True
Name: Active, dtype: bool

At first glance, this looks ok but upon closer inspection, there is a big problem. All values were interpreted as True but the last customer has an Active flag of N so this does not seem right.

Beperking ASTYPE

The takeaway from this section is that astype() will only work if:

• the data is clean and can be simply interpreted as a number
• you want to convert a numeric value to a string object

If the data has non-numeric characters or is not homogeneous, then astype() will not be a good choice for type conversion. You will need to do additional transforms for the type change to work correctly.
 

 Custom  Function

Since this data is a little more complex to convert, we can build a custom function that we apply to each value and convert to the appropriate data type.

vb: currency conversion

For currency conversion (of this specific data set), here is a simple function we can use:

def convert_currency(val):
    """
    Convert the string number value to a float
     - Remove $
     - Remove commas
     - Convert to float type
    """
    new_val = val.replace(',','').replace('$', '')
    return float(new_val) 

The code uses python’s string functions to strip out the ‘$” and ‘,’ and then convert the value to a floating point number. In this specific case, we could convert the values to integers as well but I’m choosing to use floating point in this case.

note1

I also suspect that someone will recommend that we use a Decimal type for currency. This is not a native data type in pandas so I am purposely sticking with the float approach.

note 2

Also of note, is that the function converts the number to a python float but pandas internally converts it to a float64. As mentioned earlier, I recommend that you allow pandas to convert to specific size float or int as it determines appropriate. There is no need for you to try to downcast to a smaller or upcast to a larger byte size unless you really know why you need to do it.


Now, we can use the pandas apply function to apply this to all the values in the 2016 column.

df['2016'].apply(convert_currency)

0    125000.0
1    920000.0
2     50000.0
3    350000.0
4     15000.0
Name: 2016, dtype: float64

ll the values are showing as float64 so we can do all the math functions we need to.

Zou ook met een lambda functie kunnen

df['2016'].apply(lambda x: x.replace('$', '').replace(',', '')).astype('float')

vb2 : fixing the Percent Growth column.

Using the lambda :

df['Percent Growth'].apply(lambda x: x.replace('%', '')).astype('float') / 100 

Doing the same thing with a custom function:

def convert_percent(val):
    """
    Convert the percentage string to an actual floating point percent
    - Remove %
    - Divide by 100 to make decimal
    """
    new_val = val.replace('%', '')
    return float(new_val) / 100

df['Percent Growth'].apply(convert_percent) 

Both produce the same value:
0 0.30 1 0.10 2 0.25 3 0.04 4 -0.15 Name: Percent Growth, dtype: float64

vb3: to convert the active column to a boolean

using np.where() to convert the active column to a boolean. There are several possible ways to solve this specific problem.
The np.where()  approach is useful for many types of problems so I’m choosing to include
it here.

The basic idea is to use the np.where() function to convert all “Y” values to True and everything else assigned 
False 

df["Active"] = np.where(df["Active"] == "Y", True, False) 

Whether you choose to use a lambda function, create a more standard python function or use another approach like np.where() , these approaches are very flexible and can be customized for your own unique data needs.

Pandas helper functions

Pandas has a middle ground between the blunt astype() function and the more complex custom functions. These helper functions can be very useful for certain data type conversions.

If you have been following along, you’ll notice that I have not done anything with the date columns or the Jan Units column. Both of these can be converted simply using built in pandas functions such as pd.to_numeric() and pd.to_datetime() .

The reason the Jan Units conversion is problematic is the inclusion of a non-numeric value in the column. If we tried to use astype() we would get an error (as described earlier). The pd.to_numeric() function can handle these values more gracefully:

pd.to_numeric(df['Jan Units'], errors='coerce')

0    500.0
1    700.0
2    125.0
3     75.0
4      NaN
Name: Jan Units, dtype: float64

There are a couple of items of note. First, the function easily processes the data and creates a float64 column. Additionally, it replaces the invalid “Closed” value with a NaN value because we passed errors=coerce . We can leave that value there or fill it in with a 0 using fillna(0) :

pd.to_numeric(df['Jan Units'], errors='coerce').fillna(0)

0    500.0
1    700.0
2    125.0
3     75.0
4      0.0
Name: Jan Units, dtype: float64

The final conversion I will cover is converting the separate month, day and year columns into a datetime . The pandas pd.to_datetime() function is quite configurable but also pretty smart by default.

pd.to_datetime(df[['Month', 'Day', 'Year']])

0   2015-01-10
1   2014-06-15
2   2016-03-29
3   2015-10-27
4   2014-02-02
dtype: datetime64[ns]

In this case, the function combines the columns into a new series of the appropriate datateime64 dtype.

We need to make sure to assign these values back to the dataframe:

df["Start_Date"] = pd.to_datetime(df[['Month', 'Day', 'Year']])
df["Jan Units"] = pd.to_numeric(df['Jan Units'], errors='coerce').fillna(0)

	Customer Number	Customer Name	2016	2017	Percent Growth	Jan Units	Month	Day	Year	Active	Start_Date
0	10002	Quest Industries	125000.0	162500.0	0.30	500.0	1	10	2015	True	2015-01-10
1	552278	Smith Plumbing	920000.0	1012000.0	0.10	700.0	6	15	2014	True	2014-06-15
2	23477	ACME Industrial	50000.0	62500.0	0.25	125.0	3	29	2016	True	2016-03-29
3	24900	Brekke LTD	350000.0	490000.0	0.04	75.0	10	27	2015	True	2015-10-27
4	651029	Harbor Co	15000.0	12750.0	-0.15	NaN	2	2	2014	False	2014-02-02

Now the data is properly converted to all the types we need:

df.dtypes

Customer Number int64 Customer Name object 2016 float64 2017 float64 Percent Growth float64 Jan Units float64 Month int64 Day int64 Year int64 Active bool Start_Date datetime64[ns] The dataframe is ready for analysis!

Bringing it all together

The basic concepts of using astype() and custom functions can be included very early in the data intake process. If you have a data file that you intend to process repeatedly and it always comes in the same format, you can define the dtype and converters to be applied when reading the data.

dtype

 It is helpful to think of dtype as performing astype() on the data.

converters

 The converters arguments allow you to apply functions to the various input columns similar to the approaches outlined above.

It is important to note that you can only apply a dtype or a converter function to a specified column once using this approach. If you try to apply both to the same column, then the dtype will be skipped.


Here is a streamlined example that does almost all of the conversion at the time the data is read into the dataframe:

df_2 = pd.read_csv("sales_data_types.csv",
                   dtype={'Customer Number': 'int'},
                   converters={'2016': convert_currency,
                               '2017': convert_currency,
                               'Percent Growth': convert_percent,
                               'Jan Units': lambda x: pd.to_numeric(x, errors='coerce'),
                               'Active': lambda x: np.where(x == "Y", True, False)
                              })

df_2.dtypes

Customer Number      int64
Customer Name       object
2016               float64
2017               float64
Percent Growth     float64
Jan Units          float64
Month                int64
Day                  int64
Year                 int64
Active              object
dtype: object

As mentioned earlier, I chose to include a lambda example as well as the function example for converting data. The only function that can not be applied here is the conversion of the Month , Day and Year columns to the corresponding datetime column. Still, this is a powerful convention that can help improve your data processing pipeline.

Handige pythonlinks

hoe om te gaan met configuraties in Python

https://hackernoon.com/4-ways-to-manage-the-configuration-in-python-4623049e841b

superhandige videos bijv over local and global

http://www.pythonbytesize.com/user-defined-functions.html

Difference between map, applymap and apply methods in Pandas

Summing up, 

1. apply works on a row / column basis of a DataFrame
2. applymap works element-wise on a DataFrame
3. map works element-wise on a Series

APPLY

Another frequent operation is applying a function on 1D arrays to each column or row. DataFrame’s apply method does exactly this:

In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [117]: frame
Out[117]:
               b         d         e
Utah   -0.029638  1.081563  1.280300
Ohio    0.647747  0.831136 -1.549481
Texas   0.513416 -0.884417  0.195343
Oregon -0.485454 -0.477388 -0.309548

In [118]: f = lambda x: x.max() - x.min()

In [119]: frame.apply(f)
Out[119]:
b    1.133201
d    1.965980
e    2.829781
dtype: float64

APPLYMAP

Many of the most common array statistics (like sum and mean) are DataFrame methods, so using apply is not necessary.

Element-wise Python functions can be used, too. Suppose you wanted to compute a formatted string from each floating point value in frame. You can do this with applymap:

In [120]: format = lambda x: '%.2f' % x

In [121]: frame.applymap(format)
Out[121]:
            b      d      e
Utah    -0.03   1.08   1.28
Ohio     0.65   0.83  -1.55
Texas    0.51  -0.88   0.20
Oregon  -0.49  -0.48  -0.31

MAP

The reason for the name applymap is that Series has a map method for applying an element-wise function:

In [122]: frame['e'].map(format)
Out[122]:
Utah       1.28
Ohio      -1.55
Texas      0.20
Oregon    -0.31
Name: e, dtype: object

Summing up, apply works on a row / column basis of a DataFrame, applymap works element-wise on a DataFrame, and map works element-wise on a Series.

select pandas dataframe rows and columns using LOC

The Pandas loc indexer can be used with DataFrames for two different use cases:

    a.) Selecting rows by label/index
    b.) Selecting rows with a boolean / conditional lookup

a.) Selecting rows by label/index

The loc indexer is used with the same syntax as iloc: data.loc[<row selection>, <column selection>] .

Selections met LOC zijn gebaseerd op de index van het dataframe (als die er is)

met set_index kan je index op dataframe zetten

data.set_index("last_name", inplace=True)

als de index gezet is kan je direct rijen selecteren via de last_name

1 rij select:
- data.loc['Andrade']    >>>>> series

2 rijen select
- data.loc[['Andrade','Veness']]  >>>>>>>   dataframe


Select columns with .loc using the names of the columns. In most of my data work, typically I have named columns, and use these named selections.

When using the .loc indexer, columns are referred to by names using lists of strings, or “:” slices.

# Select rows with index values 'Andrade' and 'Veness', with all columns between 'city' and 'email'
data.loc[['Andrade', 'Veness'], 'city':'email']
# Select same rows, with just 'first_name', 'address' and 'city' columns
data.loc['Andrade':'Veness', ['first_name', 'address', 'city']]

# Change the index to be based on the 'id' column
data.set_index('id', inplace=True)
# select the row with 'id' = 487
data.loc[487]

Note that in the last example, data.loc[487] (the row with index value 487) is not equal to data.iloc[487] (the 487th row in the data). The index of the DataFrame can be out of numeric order, and/or a string or multi-value.

  b.) Selecting rows with a boolean / conditional lookup

 Conditional selections with boolean arrays using data.loc[<selection>] is the most common method that people use with Pandas DataFrames. With boolean indexing or logical selection, you pass an array or Series of True/False values to the .loc indexer to select the rows where your Series has True values.

In most use cases, you will make selections based on the values of different columns in your data set.

For example, the statement data[‘first_name’] == ‘Antonio’] produces a Pandas Series with a True/False value for every row in the ‘data’ DataFrame, where there are “True” values for the rows where the first_name is “Antonio”. These type of boolean arrays can be passed directly to the .loc indexer as so:

data.loc[data['first_name] == 'Antonio'] 

a second argument can be passed to .loc to select particular columns out of the data frame. Again, columns are referred to by name for the loc indexer and can be a single string, a list of columns, or a slice “:” operation.

data.loc[data['first_name] == 'Erasmo', ['column1', 'column2', 'column3']


Selecting multiple columns with loc can be achieved by passing column names to the second argument of .loc[]

Let op welke datatype gereturned wordt

data.loc[data['first_name] == 'Antonio']   ======> SERIES

data.loc[data['first_name] == 'Antonio'] data.loc[data['first_name] == 'Erasmo', ['column1', 'column2', 'column3'] =======> DATAFRAME

Voorbeelden
# Select rows with first name Antonio, # and all columns between 'city' and 'email'

data.loc[data['first_name'] == 'Antonio', 'city':'email']

# Select rows where the email column ends with 'hotmail.com', include all columns

data.loc[data['email'].str.endswith("hotmail.com")]

# Select rows with last_name equal to some values, all columns

data.loc[data['first_name'].isin(['France', 'Tyisha', 'Eric'])]

# Select rows with first name Antonio AND hotmail email addresses

data.loc[data['email'].str.endswith("gmail.com") & (data['first_name'] == 'Antonio')]

# select rows with id column between 100 and 200, and just return 'postal' and 'web' columns

data.loc[(data['id'] > 100) & (data['id'] <= 200), ['postal', 'web']]

# A lambda function that yields True/False values can also be used.

# Select rows where the company name has 4 words in it.

data.loc[data['company_name'].apply(lambda x: len(x.split(' ')) == 4)]

# Selections can be achieved outside of the main .loc for clarity:

# Form a separate variable with your selections:

idx = data['company_name'].apply(lambda x: len(x.split(' ')) == 4)

# Select only the True values in 'idx' and only the 3 columns specified:

data.loc[idx, ['email', 'first_name', 'company']]
'

select pandas dataframe rows and columns using iloc

zie https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/

Selection Options

There’s three main options to achieve the selection and indexing activities in Pandas,

•     Selecting data by row numbers (.iloc)
•     Selecting data by label or by a conditional statment (.loc)
•     Selecting in a hybrid approach (.ix) (now Deprecated in Pandas 0.20.1)

 ILOC

 integer-location based indexing/selection

data.iloc[<row selection>, <column selection>]

Each row has a row number from 0 to the total rows (data.shape[0])  and iloc[] allows selections based on these numbers. The same applies for columns (ranging from 0 to data.shape[1] )

# Single selections using iloc and DataFrame

# Rows:
data.iloc[0] # first row of data frame (Aleshia Tomkiewicz) - Note a Series data type output.
data.iloc[1] # second row of data frame (Evan Zigomalas)
data.iloc[-1] # last row of data frame (Mi Richan)
 
# Columns:
data.iloc[:,0] # first column of data frame (first_name)
data.iloc[:,1] # second column of data frame (last_name)
data.iloc[:,-1] # last column of data frame (id) 


# Multiple row and column selections using iloc and DataFrame
data.iloc[0:5] # first five rows of dataframe
data.iloc[:, 0:2] # first two columns of data frame with all rows
data.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns.
data.iloc[0:5, 5:8] # first 5 rows and 5th, 6th, 7th columns of data frame (county -> phone1).

When selecting multiple columns or multiple rows in this manner, remember that in your selection e.g.[1:5], the rows/columns selected will run from the first number to one minus the second number. e.g. [1:5] will go 1,2,3,4., [x,y] goes from x to y-1.

Note that .iloc returns a Pandas Series when one row is selected, and a Pandas DataFrame when multiple rows are selected, or if any column in full is selected. To counter this, pass a single-valued list if you require DataFrame output.

statistiek 2

Centrummaten
Er zijn drie maten om het centrum van een verdeling te beschrijven: De modus geeft de klasse aan met de meeste waarnemingen, de mediaan geeft de klasse aan die de onderste 50% van de bovenste 50% scheidt en het gemiddelde houdt niet alleen rekening met de aantallen, maar ook met de hoogte van elke score…


Extremen uit een distributie kan je weghalen door de gebruik te maken van de interkwartiel range: alleen 2e en 3e kwartiel rondom mediaan

een frequency distribution kan je gebruiken om aan te geven hoe vaak een score voorkomt. Je kan het ook gebruiken om de waarschijnlijkheid van een score te bepalen

Bij een normaal verdeling heb je tabellen waarmee je de waarschijnlijkheid kan opzoeken dat iets voorkomt. Je moet de normaal verdeling wel omzetten naar een z-score

belangrijke waardes
z ligt tussen -1.96  en 1.96  (95% van de scores )  2.5% aan beide kanten wordt er afgehakt



We've talked a little about the difference between working with a full population of data and working with a sample. In most real-world scenarios, you won't have access to the full population. For example, you're unlikely to have rainfall measures for everyday ever; and even if you did, that's a lot of data to try and manage.
 Generally you work with samples of data that are representative of the data, and you use sample statistics such as the mean and standard deviation to approximate the parameters of the full data population. In practice it's best to get as large a sample as you can. The larger the sample, the better it will approximate the distribution and parameters of the full population.

Another thing you can do is to take multiple random samples. Each sample has a sample mean, and you can record these to form what's called a sampling distribution. With enough samples, two things happen.
One is that, thanks to something called the central limit theorem, the sampling distribution takes on a normal shape regardless of the shape of the population distribution; and the second thing is that the mean of the sampling distribution, in other words the mean of all the sample means, will be the same as the population mean.

BODS aanroepen SQL en PLSQL procedure

In BODS script oracle aanroepen


sql('OJZSTGA1_BTL',' begin createPivotTable(\'CONTROLELIJST_BESCHIKTE_VOORZIENING_JGD_2017\',\'voorzieningsoort\',\'c\'); end; ');

sql('OJZSTGA1_BTL','begin suffie(); end;');



sql('OJZSTGA1', 'truncate table DWH_DOEL.JW_STG_GEBIED_WIJK');