I'm having a csv file like below. I need to check whether the number of columns are greater than the max length of rows. Ex,
name,age,profession
"a","24","teacher","cake"
"b",31,"Doctor",""
"c",27,"Engineer","tea"
If i try to read it using
print(pd.read_csv('test.csv'))
it will print as below.
name age profession
a 24 teacher cake
b 31 Doctor NaN
c 27 Engineer tea
But it's wrong. It happened due to the less number of columns. So i need to identify this scenario as a wrong csv format. what is the best way to test this other than reading this as string and testing the length of each row.
And important thing is, the columns can be different. There are no any mandatory columns to present.
You can try put header=None into .read_csv. Then pandas will throw ParserError if number of columns won't match length of rows. For example:
try:
df = pd.read_csv("your_file.csv", header=None)
except pd.errors.ParserError:
print("File Invalid")
Related
Today my problem is this: I have a dataframe of 300 X 41. Its encoded with numbers. I want to append an 'a' to each value in the dataframe so that another down stream program will not fuss about these being 'continuous variables' which they arent, they are factors. Simple right?
Every way I can think to do this though returns a dataframe or object that is not 300x 41...but just one long list of altered values:
Please end this headache for me. How can I do this in a way that returns a 400 X 31 altered output?
> dim(x)
[1] 300 41
>x2 <- sub("^","a",x)
>dim(x2)
[1] 12300 1
Lets say i have Dataframe, which has 200 values, prices for products. I want to run some operation on this dataframe, like calculate average price for last 10 prices.
The way i understand it, right now pandas will go through every single row and calculate average for each row. Ie first 9 rows will be Nan, then from 10-200, it would calculate average for each row.
My issue is that i need to do a lot of these calculations and performance is an issue. For that reason, i would want to run the average only on say on last 10 values (dont need more) from all values, while i want to keep those values in the dataframe. Ie i dont want to get rid of those values or create new Dataframe.
I just essentially want to do calculation on less data, so it is faster.
Is something like that possible? Hopefully the question is clear.
Building off Chicodelarose's answer, you can achieve this in a more "pandas-like" syntax.
Defining your df as follows, we get 200 prices up to within [0, 1000).
df = pd.DataFrame((np.random.rand(200) * 1000.).round(decimals=2), columns=["price"])
The bit you're looking for, though, would the following:
def add10(n: float) -> float:
"""An exceptionally simple function to demonstrate you can set
values, too.
"""
return n + 10
df["price"].iloc[-12:] = df["price"].iloc[-12:].apply(add10)
Of course, you can also use these selections to return something else without setting values, too.
>>> df["price"].iloc[-12:].mean().round(decimals=2)
309.63 # this will, of course, be different as we're using random numbers
The primary justification for this approach lies in the use of pandas tooling. Say you want to operate over a subset of your data with multiple columns, you simply need to adjust your .apply(...) to contain an axis parameter, as follows: .apply(fn, axis=1).
This becomes much more readable the longer you spend in pandas. 🙂
Given a dataframe like the following:
Price
0 197.45
1 59.30
2 131.63
3 127.22
4 35.22
.. ...
195 73.05
196 47.73
197 107.58
198 162.31
199 195.02
[200 rows x 1 columns]
Call the following to obtain the mean over the last n rows of the dataframe:
def mean_over_n_last_rows(df, n, colname):
return df.iloc[-n:][colname].mean().round(decimals=2)
print(mean_over_n_last_rows(df, 2, "Price"))
Output:
178.67
I have a data frame, lets say xyz. I have written code to find out the % of null values each column possess in the dataframe. my code below:
round(100*(xyz.isnull().sum()/len(xyz.index)), 2)
let say i got following results:
abc 26.63
def 36.58
ghi 78.46
I want to drop column ghi because it has more than 70% of null values.
I achieved it using the following code:
xyz = xyz.drop(xyz.loc[:,round(100*(xyz.isnull().sum()/len(xyz.index)), 2)>70].columns, 1)
but , i did not understand how does this code works, can anyone please explain it?
the code is doing the following:
xyz.drop( [...], 1)
removes the specified elements for a given axis, either by row or by column. In this particular case, df.drop( ..., 1) means you're dropping by axis 1, i.e, column
xyz.loc[:, ... ].columns
will return a list with the column names resulting from your slicing condition
round(100*(xyz.isnull().sum()/len(xyz.index)), 2)>70
this instruction is counting the number of nulls, adding them up and normalizing by the number of rows, effectively computing the percentage of nan in each column. Then, the amount is rounded to have only 2 decimal positions and finally you return True is the number of nan is more than 70%. Hence, you get a mapping between columns and a True/False array.
Putting everything together: you're first producing a Boolean array that marks which columns have more than 70% nan, then, using .loc you use Boolean indexing to look only at the columns you want to drop ( nan % > 70%), then using .columns you recover the name of such columns, which then are used by the .drop instruction.
Hopefully this clear things up!
If you code is hard to understand , you can just check dropna with thresh, since pandas already cover this case.
df=df.dropna(axis=1,thresh=round(len(df)*0.3))
I have a dataset that indicates date & time in 5-digit format: ddd + hm
ddd part starts from 2009 Jan 1. Since the data was collected only from then to 2-years period, its [min, max] would be [1, 365 x 2 = 730].
Data is observed in 30-min interval, making 24 hrs per day period to lengthen to 48 at max. So [min, max] for hm at [1, 48].
Following is the excerpt of daycode.csv file that contains ddd part of the daycode, matching date & hm part of the daycode, matching time.
And I think I agreed to not showing the dataset which is from ISSDA. So..I will just describe that the daycode in the File1.txt file reads like '63317'.
This link gave me a glimpse of how to approach this problem, and I was in the middle of putting up this code together..which of course won't work at this point.
consume = pd.read_csv("data/File1.txt", sep= ' ', encoding = "utf-8", names =['meter', 'daycode', 'val'])
df1= pd.read_csv("data/daycode.csv", encoding = "cp1252", names =['code', 'print'])
test = consume[consume['meter']==1048]
test['daycode'] = test['daycode'].map(df1.set_index('code')['print'])
plt.plot(test['daycode'], test['val'], '.')
plt.title('test of meter 1048')
plt.xlabel('daycode')
plt.ylabel('energy consumption [kWh]')
plt.show()
Not all units(thousands) have been observed at full length but 730 x 48 is a large combination to lay out on excel by hand. Tbh, not an elegant solution but I tried by dragging - it doesn't quite get it.
If I could read the first 3 digits of the column values and match with another file's column, 2 last digits with another column, then combine.. is there a way?
For the last 2 lines you can just do something like this
df['first_3_digits'] = df['col1'].map(lambda x: str(x)[:3])
df['last_2_digits'] = df['col1'].map(lambda x: str(x)[-2:])
for joining 2 dataframes
df3 = df.merge(df2,left_on=['first_3_digits','last_2_digits'],right_on=['col1_df2','col2_df2'],how='left')
I have a data in the following form:
product/productId B000EVS4TY
1 product/title Arrowhead Mills Cookie Mix, Chocolate Chip, 1...
2 product/price unknown
3 review/userId A2SRVDDDOQ8QJL
4 review/profileName MJ23447
5 review/helpfulness 2/4
6 review/score 4.0
7 review/time 1206576000
8 review/summary Delicious cookie mix
9 review/text I thought it was funny that I bought this pro...
10 product/productId B0000DF3IX
11 product/title Paprika Hungarian Sweet
12 product/price unknown
13 review/userId A244MHL2UN2EYL
14 review/profileName P. J. Whiting "book cook"
15 review/helpfulness 0/0
16 review/score 5.0
17 review/time 1127088000
I want to convert it to a dataframe such that the entries in the 1st column
product/productId
product/title
product/price
review/userId
review/profileName
review/helpfulness
review/score
review/time
review/summary
review/text
are the column headers with the values arranged corresponding to each header in the table.
I still had a tiny doubt about your file, but since both my suggestions are quite similar, I will try to address both the scenarios you might have.
In case your file doesn't actually have the line numbers inside of it, this should do it:
filepath = "./untitled.txt" # you need to change this to your file path
column_separator="\s{3,}" # we'll use a regex, I explain some caveats of this below...
# engine='python' surpresses a warning by pandas
# header=None is that so all lines are considered 'data'
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None)
df = df.set_index(0) # this takes column '0' and uses it as the dataframe index
df = df.T # this makes the data look like you were asking (goes from multiple rows+1column to multiple columns+1 row)
df = df.reset_index(drop=True) # this is just so the first row starts at index '0' instead of '1'
# you could just do the last 3 lines with:
# df = df.set_index(0).T.reset_index(drop=True)
If you do have line numbers, then we just need to do some little adjustments
filepath = "./untitled1.txt"
column_separator="\s{3,}"
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None, index_col=0)
df.set_index(1).T.reset_index(drop=True) #I did all the 3 steps in 1 line, for brevity
In this last case, I would advise you change it in order to have line numbers in all of them (in the example you provided, the numbering starts at the second line, this might be an option about how you handle headers when exporting the data in whatever tool you might be using
Regarding the regex, the caveat is that "\s{3,}" looks for any block of 3 consecutive whitespaces or more to determine the column separator. The problem here is that we'll depend a bit on the data to find the columns. For instance, if in any of the values just so happens to appear 3 consecutive spaces, pandas will raise an exception, since the line will have one more column than the others. One solution to this could be increasing it to any other 'appropriate' number, but then we still depend on the data (for instance, with more than 3, in your example, "review/text" would have enough spaces for the two columns to be identified)
edit after realising what you meant by "stacked"
Whatever "line-number scenario" you have, you'll need to make sure you always have the same number of columns for all registers and reshape the continuous dataframe with something similar to this:
number_of_columns = 10 # you'll need to make sure all "registers" do have the same number of columns otherwise this will break
new_shape = (-1,number_of_columns) # this tuple will mean "whatever number of lines", by 10 columns
final_df = pd.DataFrame(data = df.values.reshape(new_shape)
,columns=df.columns.tolist()[:-10])
Again, take notice of making sure that all lines have the same number of columns (for instance, a file with just the data you provided, assuming 10 columns, wouldn't work). Also, this solution assumes all columns will have the same name.