I'm a beginner in coding and I wrote some codes in python pandas that I didn't understand fully and need some clarification.
Lets say this is the data, DeathYear, Age, Gender and Country are all columns in an excel file.
How to plot a table with non-numeric values in python?
I saw this question and I used this command
df.groupby('Gender')['Gender'].count().plot.pie(autopct='%.2f',figsize=(5,5))
it works and gives me a pie chart of the percentage of each gender,
but the normal pie chart command that I know for numerical data looks like this
df["Gender"].plot.pie(autopct="%.2f",figsize=(5,5))
My question is why did we add the .count()?
is it to transform non numerical data to numerical?
and why did why use the group by and type the column twice ('Gender')['Gender']?
I'll address the second part of your question first since it makes more sense to explain it that way
The reason that you use ('Gender')['Gender'] is that it does two different things. The first ('Gender') is the argument to the groupby function. It tells you that you want the DataFrame to be grouped by the 'Gender' column. Note that the groupby function needs to have a column or level to group by or else it will not work.
The second ['Gender'] tells you to only look at the 'Gender' column in the resulting DataFrame. The easiest way to see what the second ['Gender'] does is to compare the output of df.groupby('Gender').count() and df.groupby('Gender')['Gender'].count() and see what happens.
One detail that I omitted in first part for clarity it that the output of df.groupby('Gender') is not a DataFrame, but actually a DataFrameGroupBy object. The details of what exactly this object is are not important to your question, but the key is that to get a DataFrame back you need to have a function that tells you what to put in the rows of the DataFrame that you wish to create. The .count() function is one of those options (along with many others such as .mean(), etc.). In your case, since you want the total counts to make a pie chart, the .count() function does exactly that; it will count the number of times 'Female' and 'Male' appears in the 'Gender' column and that sum will be the entries in the corresponding row. The DataFrame is then able to be used to create a pie chart. So you are correct in that the .count() function transforms the non-numeric 'Female' and 'Male' entries into a numeric value which corresponds to how often those entries appeared in the initial DataFrame.
Related
So I've been working on data classification as part of a research project but since there are thousands of different values, I thought it best to use python to simplify the process rather than going through each record and classifying it manually.
So basically, I have a dataframe wherein one column is entitled "description" and another is entitled "codes". Each row in the "description" column contains a survey response about activities. The descriptions are all different but might contain some keywords. I have a list of some 40 codes to classify each row based on the text. I was thinking of manually creating some columns in the csv file and in each column, typing a keyword corresponding to each of the codes. Then, a loop (or function with a loop) is applied to the dataframe that goes through each row and if a specific substring is found that corresponds to any of the keywords, and then updated the "codes" column with the code corresponding to that keyword.
My Dilemma
For example:
Suppose the list of codes is "Dance", "Nap", "Run", and "Fight" that are in a separate dataframe column. This dataframe also with the manually entered keyword columns is shown below (can be more than two but I just used two for illustration purposes).
This dataframe is named "classes".
category
Keyword1
Keyword2
Dance
dance
danc
Nap
sleep
slept
Run
run
quick
Fight
kick
unch
The other dataframe is as follows with the "codes" column initially blank.
This dataframe is named "data".
description
codes
Iwasdancingthen
She Slept
He was stealing
The function or loop will search through the "description" column above and check if the keywords are in a given row. If they are, the corresponding codes are applied (as shown in the resulting dataframe below in bold). If not, the row in the "codes" column is left blank. The loop should run as many times as there are Keyword columns; the loop will run twice in this case since there are two keyword columns.
description
codes
Iwasdancingthen
Dance
She Slept
Sleep
He landed a kick
Fight
We are family
FYI: The keywords don't actually have to be complete words. I'd like to use partial words too as you see above.
Also, it should be noted that the loop or function I want to make should account for case sensitivity and strings that are combined.
I hope you understand what I'm trying to do.
What I tried:
At first, I tried using a dictionary and manipulate it somehow. I used the advice here:
search keywords in dataframe cell
However, this didn't work too well as I had many "Nan" values pop up and it became too complicated, so I tried a different route using lists. The code I used was based off another user's advice:
How to conditionally update DataFrame column in Pandas
Here's what I did:
# Create lists from the classes dataframe
Keyword1list = classes["Keyword1"].values.tolist()
Category = classes["category"].values.tolist()
I then used the following loop for classification
for i in range(len(Keyword1list)):
data.loc[data["description"] == Keyword1list[i] , "codes"] = Category[i]
However, the resulting output still gives me "Nan" for all columns. Also, I don't know how to loop over every single keyword column (in this case, loop over the two columns "Keyword1" and "Keyword2").
I'd really appreciate it if anyone could help me with a function or loop that works. Thanks in advance!
Edit: It was pointed out to me that some descriptions might contain multiple keywords. I forgot to mention that the codes in the "classes" dataframe are ordered by rank so that the ones that appear first on the dataframe should take priority; for example, if both "dance" and "nap" are in a description, the code listed higher in the "classes" dataframe (i.e. dance) should be selected and inputted into the "codes" column. I hope there's a way to do that.
I've been poking around a bit and can't see to find a close solution to this one:
I'm trying to transform a dataframe from this:
To this:
Such that remark_code_names with similar denial_amounts are provided new columns based on their corresponding har_id and reason_code_name.
I've tried a few things, including a groupby function, which gets me halfway there.
denials.groupby(['har_id','reason_code_name','denial_amount']).count().reset_index()
But this obviously leaves out the reason_code_names that I need.
Here's a minimum:
pd.DataFrame({'har_id':['A','A','A','A','A','A','A','A','A'],'reason_code_name':[16,16,16,16,16,16,16,22,22],
'remark_code_name':['MA04','N130','N341','N362','N517','N657','N95','MA04','N341'],
'denial_amount':[5402,8507,5402,8507,8507,8507,8507,5402,5402]})
Using groupby() is a good way to go. Use it along with transform() and overwrite the column with name 'remark_code_name. This solution puts all remark_code_names together in the same column.
denials['remark_code_name'] = denials.groupby(['har_id','reason_code_name','denial_amount'])['remark_code_name'].transform(lambda x : ' '.join(x))
denials.drop_duplicates(inplace=True)
If you really need to create each code in their own columns, you could apply another function and use .split(). However you will first need to set the number of columns depending on the max number of codes you find in a single row.
I'm new to Python and after a lot of tinkering, have managed to clean up some .csv data.
I now have a bunch of countries as rows and a bunch of dates as columns, and am trying to create a chart showing a line for each country's value over time.
The problem is that when I enter df.plot() it results in a chart with each date as a line.
I have melted the data such that the first column is country, second is date, and third is value, but all I get is a single blue block growing over time (not multiple lines). How can I fix this?
You can use the transpose function in [pandas][1]:
Or instead of df.plot, you can use plot(coloumn, row).
As it was mentioned in comments, it is always better to provide an example (look at #importanceofbeingeenest comment).
I have csv file
salary = pd.read_csv('./datasets/salary.csv')
is it possible to have an output like this
Apology for sharing only the concept as you did not provide any code in the question. Consider adding example code if you are unable to understand the concept.
This will require creating a new "column" - "Label" in the dataframe for each matching "Salary". For example, check the table in the link below:
Click to see a sample table to achieve desired columns
This columns "Label" can be filled using ifesle statements. Use string function or == "string in the column Salary" to write the conditional statements. Additionally, use for loop if dataframe has multiple entries for each type of salary. Second, create the three new columns of interest i.e. "per annum", "p.a. + Super", and "p.d.". Now, use ifesle statement again on Label column to enter values row wise in each column of interest based on the conditional statement.
This should let you achieve the desired entries.
Hope it helps.
I'm a veteran of Pandas DataFrame objects, but I'm struggling to find a clean, convenient method for altering the values in a Dask DataFrame column. For a specific example, I'm trying to multiply positive values in a numpy.float column by -1, thereby making them negative. Here is my current method (I'm trying to change the last column in the DataFrame):
cols = df.columns
df[[cols[-1]]] = df[[cols[-1]]]*-1
This seems to work only if the column has a string header, otherwise it adds another column using the index number as a string-type column name for a new column. Is there something akin to the Pandas method of, say, df.iloc[-1,:] = df.iloc[-1,:]*-1 that I can use with a Dask dataframe?
Edit: I'm also trying to implement: df = df.applymap(lambda x: x*-1). This, of course, applies the function to the entire dataframe, but is there a way to apply a function over just one column? Thank you.
first question
If something works for string columns and not for numeric-named columns then that is probably a bug. I recommend raising an issue at https://github.com/dask/dask/issues/new
second question
but is there a way to apply a function over just one column?
You can't apply a single Python function over a dask dataframe that is stored in many pieces directly, however methods like .map_partitions or .reduction may help you to achieve the same result with some cleverness.
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