I'm looking through a UCI Adult dataframe (https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data). I want to output and count all the rows, where native country is 'Germany'. The following code:
df[df['native-country']=="Germany"]
Says me that all the rows are False. Is there any other way to count the amount of rows and/or print them out? Dummie might not be an option, since there are more than 20 different countries in the dataframe.
I think you have white blank in the country field
Try
df[df['native-country']==" Germany"]
Or
df[df['native-country'].str.contains("Germany")]
Your command df[df['native-country']=="Germany"] should already print only rows that match the condition. If you're seeing rows of False values, you might actually be executing df['native-country']=="Germany", which returns a boolean mask of True and False.
To count the occurrences of each unique value in the native-country column, try:
df['native-country'].value_counts()
Related
I'm trying to write a python script that finds unique values (names) and reports the frequency of their occurrence, making use of Pandas library. There's a total of around 90 unique names, which I've anonymised in the head of the dataframe pasted below.
,1,2,3,4,5
0,monday09-01-2022,tuesday10-01-2022,wednesday11-01-2022,thursday12-01-2022,friday13-01-2022
1,Anonymous 1,Anonymous 1,Anonymous 1,Anonymous 1,
2,Anonymous 2,Anonymous 4,Anonymous 5,Anonymous 5,Anonymous 5
3,Anonymous 3,Anonymous 3,,Anonymous 6,Anonymous 3
4,,,,,
I'm trying to drop any row (the full row) that contains the regex expression "^monday.*", intending to indicate the word "monday" followed by any other number of random characters. I want to drop/deselect any cell/value within that row.
To achieve this goal, I've tried using the line of code below (and many other approaches I found on SO).
df = df[df[1].str.contains("^monday.*", case = True, regex=True) == False]
To clarify, I'm trying to search values of column "1" for the value "^.monday.*" and then deselecting the rows and all values in that row that match the regex expression. I've succesfully removed "monday09-01-2022" and "tuesday10-01-2022" etc.. but I'm also losing random names that are not in the matching rows.
Any help would be very much appreciated! Thank you!
I am working with some weather data that is missing some values (indicated via value code). For example, if SLP data is missing, it is assigned code 99999. I was able to use a window function to calculate a 7 day average and save it as a new column. A significantly reduced example of a single row is shown below:
SLP_ORIGIN
SLP_ORIGIN_7DAY_AVG
99999
11945.823516044207
I'm trying to write code such that when SLP_ORIGIN has the missing code it gets replaced using the SLP_ORIGIN_7DAY_AVG value. However, most code explains how to replace a column value based on a conditional with a constant value, not the column value. I tried using the following:
train_impute = train.withColumn("SLP_ORIGIN", \
when(train["SLP_ORIGIN"] == 99999, train["SLP_ORIGIN_7DAY_AVG"]).otherwise(train["SLP_ORIGIN"]))
where the dataframe is called train.
When I perform a count on the SLP_ORIGIN column using train.where("SLP_ORIGIN = 99999").count() I get the same count from before I attempted replacing the value in that column. I have already checked and my SLP_ORIGIN_7DAY_AVG does not have any values that match the missing code.
So how do I actually replace the 99999 values in the SLP_ORIGIN column with the associated SLP_ORIGIN_7DAY_AVG value?
EVEN BETTER, is there a way to do this replacement and window calculation without making a 7 day average column (I have other variables I need to do the same thing with so I'm hoping there is a more efficient way to do this).
Make sure to double check with dataframe you are verifying on.
I was using train.where("SLP_ORIGIN = 99999").count() when I should have been using train_impute.where("SLP_ORIGIN = 99999").count()
Additionally, instead of making a whole new column to store the imputed 7 day average, one can only calculate the average when the missing value code is present:
train = train.withColumn("SLP_ORIGIN", when(train["SLP_ORIGIN"] == 99999, f.avg('SLP_ORIGIN').over(w)).otherwise(train["SLP_ORIGIN"]))\
First of all thanks in advance, there are always answers here so we learn a lot from the experts. I'm a noob using "pandas" (it's super handie for what i tried and achieved so far).
I have these data, handed to me like this (don't have access to the origin), 20k rows or more sometimes. The 'in' and 'out' columns may have one or more data per date, so when i get a 'in' the next data could be a 'out' or a 'in', depending, leaving me a blank cell, that's the problem (see first image).
I want to filter the first datetime-in, to left it in one column and the last datetime-out in another but the two in one row (see second image); the data comes in a csv file. I am doing this particular work manually with LibreOffice Calc (yeap).
So far, I have tried locating and relocating, tried merging, grouping... nothing works for me so i feel frustrated, ¿would you please lend me a hand? here is a minimal sample of the file
By the way english is not my language. ¡Thanks so much!
First:
out_column = df["out"].tolist()
This gives you all the out dates as a list, we will need that later.
in_column = df["in"].tolist() # in is used by python so I suggest renaming that row
I treat NaT as NaN (Null) in this Case.
Now we have to find what rows to keep, which we do by going through the in column and only keeping the rows after a NaN (and the first one):
filtered_df = []
tracker = False
for index, element in enumerate(in):
if index == 0 or tracker is True:
filtered_df.append(True)
tracker = False
continue
if element is None:
tracker = True
filtered_df.append(False)
Then you filter your df by this Boolean List:
df = df[filtered_df]
Now you fix up your out column by removing the null values:
while null in out_column:
out_column.remove(null)
Last but not least you overwrite your old out column with the new one:
df["out"] = out_column
I have a DataFrame as shown in the attached image. My columns of interest are fgr and fgr1. As you can see, they both contain values corresponding to years.
I want to iterate in the the two columns and for any value present, I want 1 if the value is present or else 0.
For example, in fgr the first value is 2028. So, the first row in column 2028 will have a value 1 and all other columns have value 0.
I tried using lookup but I did not succeed. So, any pointers will be really helpful.
Example dataframe
Data:
Data file in Excel
This fill do you job. You can use for loops aswell but I think this approach will be faster.
df["Matched"] = df["fgr"].isin(df["fgr1"])*1
Basically you check if values from one are in anoter column and if they are, you get True or False. You then multiply by 1 to get 1 and 0 instead of True or False.
From this answer
Not the most efficient, but should work for your case(time consuming if large dataset)
s = df.reset_index().melt(['index','fgr','fgr1'])
s['value'] = s.variable.eq(s.fgr.str[:4]).astype(int)
s['value2'] = s.variable.eq(s.fgr1.str[:4]).astype(int)
s['final'] = np.where(s['value']+s['value2'] > 0,1,0)
yourdf = s.pivot_table(index=['index','fgr','fgr1'],columns = 'variable',values='final',aggfunc='first').reset_index(level=[1,2])
yourdf
I have a table with 4 columns filled with integer. Some of the rows have a value "null" as its more than 1000 records with this "null" value, how can I delete these rows all at once? I tried the delete method but it requires the index of the row its theres over 1000 rows. Is there as faster way to do it?
Thanks
use the 'drop.isnull()' function.
To remove a row in a datascience package:
name_of_your_table.remove() # number of the row in the bracket
Use the function:
name_of_your_table.dropna()
It will drop all the "null" values.
#df is the original dataframe#
#The '-' operator removes the null values and re-assigns the remaining ones to df#
df=idf[-(df['Column'].isnull())]
use dataframe_name.isnull() #To check the is there any missing values in your table.
use dataframe_name.isnull.sum() #To get the total number of missing values.
use dataframe_name.dropna() # To drop or delete the missing values.