I'm trying to display the total price from my TipCalculator as $147.00, but it is only returning a single 0 at the end $147.0. Is there a way to fix this?
You might be looking for DecimalFormat.
It can be used to format a number with fixed decimals, for example
val num = 147
val df = DecimalFormat("#.00")
println(df.format(num)) //prints 147.00
Related
Imagine I have a dataset that is like so:
ID birthyear weight
0 619040 1962 0.1231231
1 600161 1963 0.981742
2 25602033 1963 1.3123124
3 624870 1987 10,000
and I want to get the mean of the column weight, but the obvious 10,000 is hindering the actual mean. In this situation I cannot change the value but must work around it, this is what I've got so far, but obviously it's including that last value.
avg_num_items = df_cleaned['trans_quantity'].mean()
translist = df_cleaned['trans_quantity'].tolist()
my dataframe is df_cleaned and the column I'm actually working with is 'trans_quantity' so how do I go about the mean while working around that value?
Since you added SQL in your tags, In SQL you'd want to exclude it in the WHERE clause:
SELECT AVG(trans_quantity)
FROM your_data_base
WHERE trans_quantity <> 10,000
In Pandas:
avg_num_items = df_cleaned[df_cleaned["trans_quantity"] != 10000]["trans_quantity"].mean()
You can also replace your value with a NAN and skip it in the mean:
avg_num_items = df_cleaned["trans_quantity"].replace(10000, np.nan).mean(skipna=True)
With pandas, ensure you have numeric data (10,000 is a string), filter the values above threshold and use the mean:
(pd.to_numeric(df['weight'], errors='coerce')
.loc[lambda x: x<10000]
.mean()
)
output: 0.8057258333333334
I was experimenting with the kaggle.com Titanic data set (data on every person on the Titanic) and came up with a gender breakdown like this:
df = pd.DataFrame({'sex': ['male'] * 577 + ['female'] * 314})
gender = df.sex.value_counts()
gender
male 577
female 314
I would like to find out the percentage of each gender on the Titanic.
My approach is slightly less than ideal:
from __future__ import division
pcts = gender / gender.sum()
pcts
male 0.647587
female 0.352413
Is there a better (more idiomatic) way?
This function is implemented in pandas, actually even in value_counts(). No need to calculate :)
just type:
df.sex.value_counts(normalize=True)
which gives exactly the desired output.
Please note that value_counts() excludes NA values, so numbers might not add up to 1.
See here: http://pandas-docs.github.io/pandas-docs-travis/generated/pandas.Series.value_counts.html
(A column of a DataFrame is a Series)
In case you wish to show percentage one of the things that you might do is use value_counts(normalize=True) as answered by #fanfabbb.
With that said, for many purposes, you might want to show it in the percentage out of a hundred.
That can be achieved like so:
gender = df.sex.value_counts(normalize=True).mul(100).round(1).astype(str) + '%'
In this case, we multiply the results by hundred, round it to one decimal point and add the percentage sign.
If you want to merge counts with percentage, can use:
c = df.sex.value_counts(dropna=False)
p = df.sex.value_counts(dropna=False, normalize=True)
pd.concat([c,p], axis=1, keys=['counts', '%'])
I think I would probably do this in one go (without importing division):
1. * df.sex.value_counts() / len(df.sex)
or perhaps, remembering you want a percentage:
100. * df.sex.value_counts() / len(df.sex)
Much of a muchness really, your way looks fine too.
I'm sorry, I know this is basic but I've tried to figure it out myself for 2 days by sifting through documentation to no avail.
My code:
import numpy as np
import pandas as pd
name = ["bob","bobby","bombastic"]
age = [10,20,30]
price = [111,222,333]
share = [3,6,9]
list = [name,age,price,share]
list2 = np.transpose(list)
dftest = pd.DataFrame(list2, columns = ["name","age","price","share"])
print(dftest)
name age price share
0 bob 10 111 3
1 bobby 20 222 6
2 bombastic 30 333 9
Want to divide all elements in 'price' column with all elements in 'share' column. I've tried:
print(dftest[['price']/['share']]) - Failed
dftest['price']/dftest['share'] - Failed, unsupported operand type
dftest.loc[:,'price']/dftest.loc[:,'share'] - Failed
Wondering if I could just change everything to int or float, I tried:
dftest.astype(float) - cant convert from str to float
Ive tried iter and items methods but could not understand the printouts...
My only suspicion is to use something called iterate, which I am unable to wrap my head around despite reading other old posts...
Please help me T_T
Apologies in advance for the somewhat protracted answer, but the question is somewhat unclear with regards to what exactly you're attempting to accomplish.
If you simply want price[0]/share[0], price[1]/share[1], etc. you can just do:
dftest['price_div_share'] = dftest['price'] / dftest['share']
The issue with the operand types can be solved by:
dftest['price_div_share'] = dftest['price'].astype(float) / dftest['share'].astype(float)
You're getting the cant convert from str to float error because you're trying to call astype(float) on the ENTIRE dataframe which contains string columns.
If you want to divide each item by each item, i.e. price[0] / share[0], price[1] / share[0], price[2] / share[0], price[0] / share[1], etc. You would need to iterate through each item and append the result to a new list. You can do that pretty easily with a for loop, although it may take some time if you're working with a large dataset. It would look something like this if you simply want the result:
new_list = []
for p in dftest['price'].astype(float):
for s in dftest['share'].astype(float):
new_list.append(p/s)
If you want to get this in a new dataframe you can simply save it to a new dataframe using pd.Dataframe() method:
new_df = pd.Dataframe(new_list, columns=[price_divided_by_share])
This new dataframe would only have one column (the result, as mentioned above). If you want the information from the original dataframe as well, then you would do something like the following:
new_list = []
for n, a, p in zip(dftest['name'], dftest['age'], dftest['price'].astype(float):
for s in dftest['share'].astype(float):
new_list.append([n, a, p, s, p/s])
new_df = pd.Dataframe(new_list, columns=[name, age, price, share, price_div_by_share])
If you check the data types of your dataframe, you will realise that they are all strings/object type :
dftest.dtypes
name object
age object
price object
share object
dtype: object
first step will be to change the relevant columns to numbers - this is one way:
dftest = dftest.set_index("name").astype(float)
dftest.dtypes
age float64
price float64
share float64
dtype: object
This way you make the names a useful index, and separate it from the numeric data. This is just a suggestion; you may have other reasons to leave names as a columns - in that case, you have to individually change the data types of each column.
Once that is done, you can safely execute your code :
dftest.div(dftest.share,axis=0)
age price share
name
bob 3.333333 37.0 1.0
bobby 3.333333 37.0 1.0
bombastic 3.333333 37.0 1.0
I assume this is what you expect as your outcome. If not, you can tweak it. Main part is get your data types as numbers before computation/division can occur.
I have certain numbers within a column of my dataframe that have negative numbers in a string format like this: "500.00-" I need to convert every negative number within the column to numeric format. I'm sure there's an easy way to do this, but I have struggled finding one specific to pandas dataframe. Any help would be greatly appreciated.
I have tried the basic to_numeric function as shown below, but it doesn't read it in correctly. Also, only some of the numbers within the column are negative, therefore I can't simply remove all the negative signs and multiply the column by 1.
Q1['Credit'] = pd.to_numeric(Q1['Credit'])
Sample data:
df:
num
0 50.00
1 60.00-
2 70.00+
3 -80.00
Using series str accessor to check last digit. If it is '-' or '+', swap it to front. Use df.mask to apply it only to rows having -/+ as suffix. Finally, astype column to float
df.num.mask(df.num.str[-1].isin(['-','+']), df.num.str[-1].str.cat(df.num.str[:-1])).astype('float')
Out[1941]:
0 50.0
1 -60.0
2 70.0
3 -80.0
Name: num, dtype: float64
Possibly a bit explicit but would work
# build a mask of negative numbers
m_neg = Q1["Credit"].str.endswith("-")
# remove - signs
Q1["Credit"] = Q1["Credit"].str.rstrip("-")
# convert to number
Q1["Credit"] = pd.to_numeric(Q1["Credit"])
# Apply the mask to create the negatives
Q1.loc[m_neg, "Credit"] *= -1
Let us consider the following example dataframe:
Q1 = pd.DataFrame({'Credit':['500.00-', '100.00', '300.00-']})
Credit
0 500.00-
1 100.00
2 300.00-
We can use str.endswith to create a mask which indicates the negative numbers. Then we use np.where to conditionally convert the numbers to negative:
m1 = Q1['Credit'].str.endswith('-')
m2 = Q1['Credit'].str[:-1].astype(float)
Q1['Credit'] = np.where(m1, -m2, m2)
Output
Credit
0 -500.0
1 100.0
2 -300.0
I am writing a custom totaling method for a grid view. I am totaling fairly large numbers so I'd like to use a decimal to get the total. The problem is I need to control the maximum length of the total number. To solve this problem I started using float but it doesn't seem to support large enough numbers, I get this in the totals column(1.551538E+07). So is there some formating string I can use in .ToString() to guarentee that I never get more then X characters in the total field? Keep in mind I'm totaling integers and decimals.
If you're fine with all numbers displaying in scientific notation, you could go with "E[numberOfDecimalPlaces]" as your format string.
For example, if you want to cap your strings at, say, 12 characters, then, accounting for the one character for the decimal point and five characters needed to display the exponential part, you could do:
Function FormatDecimal(ByVal value As Decimal) As String
If value >= 0D Then
Return value.ToString("E5")
Else
' negative sign eats up another character '
Return value.ToString("E4")
End If
End Function
Here's a simple demo of this function:
Dim d(5) As Decimal
d(0) = 1.203D
d(1) = 0D
d(2) = 1231234789.432412341239873D
d(3) = 33.3218403820498320498320498234D
d(4) = -0.314453908342094D
d(5) = 000032131231285432940D
For Each value As Decimal in d
Console.WriteLine(FormatDecimal(value))
Next
Output:
1.20300E+000
0.00000E+000
1.23123E+009
3.33218E+001
-3.1445E-001
3.21312E+016
You could use Decimal.Round, but I don't understand the exact question, it sounds like you're saying that if the total adds up to 12345.67, you might only want to show 4 digits and would then show 2345 or do you just mean that you want to remove the decimals?