Develop Reference

Pandas DataFrame Float64 - Point Precision

Precisely, I have an Excel file that among other columns, I have one with float numbers.
The problem is: when trying to convert it´s values to string, Pandas looses the precision point number for some of them.
The flow is described below:
Action - Reading Data:
df = pd.read_excel(io=_file, sheet_name="History", header=4, skiprows=[5, 6, 7]).fillna("")
Action - Converting:
test = df.assign(STRING_VALUES=df.SOURCE_VALUES.map(lambda x: str(x)))
Action - Checking Pandas Decimal Pattern:
pd.get_option("styler.format.decimal")
Result: ',' (that´s correct).
Action - The Reality
Highlights:
My job is: check if SOURCE_VALUES has more than two decimals values! And with this behavior, I am getting FALSE/POSITIVE reports.
I am in Brazil, so we use the same format for (currency) float numbers as European does, thousands it is "." and for decimal ",". Apparently, the default format presented by Pandas is correct.
Conclusion:
As described in my image, not all values loose the precision point number. What s the workaround for this behavior?
Conclusion 2:
I got the real source file now in XML! And when I received the spreadsheet, it was no longer in that format, but in xlsx. In this way, Excel applied a standard format by creating a mask as usual for floating numbers.
For this reason I was intrigued, what would be the reason for some values to have the correct formatting and others not in the decimals after converting them to string.
But now, I am sure that it´s not a fault of Pandas. And the real values are "correct".
Final Thoughts
The most important lesson here is to be aware of the origin of the data, so that you don't mistrust your results!

It looks like values in excel are formatted to display 0.00 format, but real data is different. For example, if you change format of value 4556.38 to display more digits, you will see 456.379999... . With that, pandas are reading the real value, not formatted value.
To control float to str conversion format, maybe use format method (https://www.w3schools.com/python/ref_string_format.asp)

CSV cannot be interpreted by numeric values

(This is a mix between code and 'user' issue, but since i suspect the issue is code, i opted to post in StackOverflow instead of SuperUser Exchange).
I generated a .csv file with pandas.DataFrame.to_csv() method. This file consists in 2 columns: one is a label (text) and another is a numeric value called accuracy (float). The delimiter used to separate columns is comma (,) and all float values are stored with dot ponctuation like this: 0.9438245862
Even saving this column as float, Excel and Google Sheets infer its type as text. And when i try to format this column as number, they ignore "0." and return a very high value instead of decimals like:
(text) 0.9438245862 => (number) 9438245862,00
I double-checked my .csv file reimporting it again with pandas.read_csv() and printing dataframe.dtypes and the column is imported as float succesfully.
I'd thank for some guidance on what am i missing.
Thanks,

By itself, the csv file should be correct. Both you and Pandas know what delimiter and floating point format are. But Excel might not agree with you, depending on your locale. A simple way to make sure is to write a tiny Excel sheet containing on first row one text value and one floating point one. You then export the file as csv and control what delimiter and floating point formats are.
AFAIK, it is much more easy to change your Python code to follow what your Excel expects that trying to explain Excel that the format of CSV files can vary...
I know that you can change the delimiter and floating point format in the current locale in a Windows system. Simply it is a global setting...

A short example of data would be most useful here. Otherwise we have no idea what you're actually writing/reading. But I'll hazard a guess based on the information you've provided.
The pandas dataframe will have column names. These column names will be text. Unless you tell Excel/Sheets to use the first row as the column name, it will have to treat the column as text. If this isn't the case, could you perhaps save the head of the dataframe to a csv, check it in a text editor, and see how Excel/Sheets imports it. Then include those five rows and two columns in your follow up.

The coding is not necessarily the issue here, but a combination of various factors. I am assuming that your computer is not using the dot character as a decimal separator, due to your language settings (for example, French, Dutch, etc). Instead your computer (and thus also Excel) is likely using a comma as a decimal separator.
If you want to open the data of your analysis / work later with Excel with little to no changes, you can either opt to change how Excel works or how you store the data to a CSV file.
Choosing the later, you can specify the decimal character for the df.to_csv method. It has the "decimal" keyword. You should then also remember that you have to change the decimal character during the importing of your data (if you want to read again the data).
Continuing with the approach of adopting your Python code, you can use the following code snippets to change how you write the dataframe to a csv
import pandas as pd
... some transformations here ...
df.to_csv('myfile.csv', decimal=',')
If you, then, want to read that output file back in with Python (using Pandas), you can use the following:
import pandas as pd
df = pd.read_csv('myfile.csv', decimal=',')

Converting schemas via pandas vs pyarrow

I have a dataframe in pandas that I want to use pyarrow to write it out as a parquet.
I also need to be able to specify column types. If I change the type via pandas, I get no error; but when I change the it via pyarrow, I get an error. See examples:
Given
import pandas as pd
import pyarrow as pa
data = {"col": [86002575]}
df = pd.DataFrame(data)
Via Pandas
df = df.astype({"col": "float32"})
table = pa.Table.from_pandas(df)
No errors
Via PyArrow
schema = pa.Schema.from_pandas(df)
i = schema.get_field_index("col")
schema = schema.set(i, pa.field("col", pa.float32()))
table = pa.Table.from_pandas(df, schema=schema)
get error:
pyarrow.lib.ArrowInvalid: ('Integer value 86002575 not in range: -16777216 to 16777216', 'Conversion failed for column col with type int64')
I don't even recognize that range either. Is it trying to do some intermediary conversion when converting between the two?

When converting from one type to another, arrow is much stricter than pandas.
In your case you are converting from int64 to float32. Because they are limits to the exact representation of whole number in floating point, arrow limits the range you can convert to 16777216. Past that limit, the float precision gets bad and if you were to convert the float value back to an int, you are not guaranteed to have the same value.
You can easily ignore these checks though:
schema_float32 = pa.schema([pa.field("col", pa.float32())])
table = pa.Table.from_pandas(df, schema=schema_float32, safe=False)
EDIT:
It's not documented explicitely in arrow. It's common software engineering knowledge.
In wikipedia:
Any integer with absolute value less than 2^24 can be exactly
represented in the single precision format, and any integer with
absolute value less than 2^53 can be exactly represented in the double
precision format. Furthermore, a wide range of powers of 2 times such
a number can be represented. These properties are sometimes used for
purely integer data, to get 53-bit integers on platforms that have
double precision floats but only 32-bit integers.
2^24 = 16777216
It's not very well documented in arrow. You can look at the code

How to Import a .pos file as a numpy array with mixed data types?

I am rather new to Python and I am working with .pos files. They are not that common, but I can explain their structure.
There is a header with general information and then 15 different columns containing data.The first two columns contain the GPS time (the date the first column and the time in the second - standard format YYYY/MM/DD hh:mm:ss.ms), then there are 3 columns containing coordinates or distances in meters and then other columns that are other measurements, always numbers. Here can be found an example, mind only that my GPST (gps time) is as explained above.
As a matter of fact, there are three data types in this file, that are datetime, integer, and floating numbers.
I need to import this file in Python as an array. Apparently, Python can consider .pos file as a text file, so I have tried to to use the loadtext() command, specifying the different data types (datetime64, int, float). However, it gave me an error, saying that the date format could not be recognized. Then, I tried with the command genfromtext(), both specifying the data types and with dtype=None. In the first case I got empty columns for date and time and in the latter case I got the date and time as a string.
I would like the date and the time to be recognized as such and not as a string, as I will need it later on for further analyses. Does someone have an idea on how I could import this file correctly?
Please, just try to be clear because I am a neophyte programmer!
Thank you for your help.

I answer my own question, maybe it is useful to someone.
.pos file can be open using the Pandas package as follows:
import pandas as pd
df = pd.read_table(filepath, sep='\s+', parse_dates={'Timestamp': [0, 1]})
In my data, the first two columns are date and time, which is considered as such by the argument "parse_dates={'Timestamp': [0, 1]}"

not able to read currency symbol from the cell using pandas python

I am using pandas.read_excel(file) to read the file, but instead of getting number with currency symbol its giving numbers only not with currency symbol.
help will be appreciated.
thanks]1

When the Excel file is read by Pandas it reads the underlying value of the cell which fundamentally is either a string or a number. Things like currency symbols are just applied as formatting by Excel - they affect what you see on screen but don't actually 'exist' in the cell value. For example the number 1.1256 might appear as 1.1 if you select on decimal place, or £1.13 if you select currency, it could even appear as a date, but fundamentally it is just 1.1256 and this is the value Pandas reads. Generally this is useful because with numbers you can perform arithmetic whereas if pandas imported '£1.13' you would be unable to do any arithmetic (for example add this to another amount of money) until you had removed the £ symbol and converted to a decimal and in so doing you would have lost some precision (as the number is now 1.13 not 1.1256).
If you need to view the data with the currency symbol, I'd suggest just adding it on at the last moment, for example if you print the data to screen you could use print(f'£{your_number}')