I am coming from an R background and used to being able to retrieve the value from a dataframe by using syntax like:
r_dataframe$some_column_name[row_number]
And I can assign a value to the dataframe by the following syntax:
r_dataframe$some_column_name[row_number] <= some_value
or without the arrow:
r_dataframe$some_column_name[row_number] = some_value
For example:
#create R dataframe data
employee <- c('John Doe','Peter Gynn','Jolie Hope')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2010-11-1','2008-3-25','2007-3-14'))
employ.data <- data.frame(employee, salary, startdate)
#print out the name of this employee
employ.data$employee[2]
#assign the name
employ.data$employee[2] <= 'Some other name'
I'm now learning some Python and from what I can see the most straight-forward way to retreive a value from a pandas dataframe is:
pandas_dataframe['SomeColumnName'][row_number]
I can see the similarities to R.
However, what confuses me is that when it comes to modifying/assigning the value in the pandas dataframe I need to completely change the syntax to something like:
pandas_dataframe.at[row_number, 'SomeColumnName'] = some_value
To read this code is going to require a lot more concentration because the column name and row number have changed order.
Is this the only way to perform this pair of operations? Is there a more logical way to do this that respects the consistent use of column name and row number order?
If I understand what you mean correctly, as #sammywemmy mentioned you can use .loc and .iloc to get/change value in any row and column.
If the order of your dataframe rows changes, you must define index to get every row (datapoint) by its index, even if the order has changed.
Like below:
df = pd.DataFrame(index=['a', 'b', 'c'], columns=['time', 'date', 'name'])
Now you can get the first row by its index:
df.loc['a'] # equivalent to df.iloc[0]
It turns out that pandas_dataframe.at[row_number, 'SomeColumnName'] can be used to modify AND retrieve information.
Related
I have a below Dataframe with 3 columns:
df = DataFrame(query, columns=["Processid", "Processdate", "ISofficial"])
In Below code, I get Processdate based on Processid==204 (without Column Names):
result = df[df.Processid == 204].Processdate.to_string(index=False)
But I wan the same result for Two columns at once without column names, Something like below code:
result = df[df.Processid == 204].df["Processdate","ISofficial"].to_string(index=False)
I know how to get above result but I dont want Column names, Index and data type.
Can someone help?
I think you are looking for header argument in to_string parameters. Set it to False.
df[df.Processid==204][['Processdate', 'ISofficial']].to_string(index=False, header=False)
I have a multiIndex dataframe created with pandas similar to this one:
nest = {'A1': dfx[['aa','bb','cc']],
'B1':dfx[['dd']],
'C1':dfx[['ee', 'ff']]}
reform = {(outerKey, innerKey): values for outerKey, innerDict in nest.items() for innerKey, values in innerDict.items()}
dfzx = pd.DataFrame(reform)
What I am trying to achieve is to add a new row at the end of the dataframe that contains a summary of the total for the three categories represented by the new index (A1, B1, C1).
I have tried with df.loc (what I would normally use in this case) but I get error. Similarly for iloc.
a1sum = dfzx['A1'].sum().to_list()
a1sum = sum(a1sum)
b1sum = dfzx['B1'].sum().to_list()
b1sum = sum(b1sum)
c1sum = dfzx['C1'].sum().to_list()
c1sum = sum(c1sum)
totalcat = a1sum, b1sum, c1sum
newrow = ['Total', totalcat]
newrow
dfzx.loc[len(dfzx)] = newrow
ValueError: cannot set a row with mismatched columns
#Alternatively
newrow2 = ['Total', a1sum, b1sum, c1sum]
newrow2
dfzx.loc[len(dfzx)] = newrow2
ValueError: cannot set a row with mismatched columns
How can I fix the mistake? Or else is there any other function that would allow me to proceed?
Note: the DF is destined to be moved on an Excel file (I use ExcelWriter).
The type of results I want to achieve in the end is this one (gray row "SUM"
I came up with a sort of solution on my own.
I created a separate DataFrame in Pandas that contains the summary.
I used ExcelWriter to have both dataframes on the same excel worksheet.
Technically It would be then possible to style and format data in Excel (xlsxwriter or framestyle seem to be popular modules to do so). Alternatively one should be doing that manually.
I know this has been asked before but I cannot find an answer that is working for me. I have a dataframe df that contains a column age, but the values are not all integers, some are strings like 35-59. I want to drop those entries. I have tried these two solutions as suggested by kite but they both give me AttributeError: 'Series' object has no attribute 'isnumeric'
df.drop(df[df.age.isnumeric()].index, inplace=True)
df = df.query("age.isnumeric()")
df = df.reset_index(drop=True)
Additionally is there a simple way to edit the value of an entry if it matches a certain condition? For example instead of deleting rows that have age as a range of values, I could replace it with a random value within that range.
Try with:
df.drop(df[df.age.str.isnumeric() == False].index, inplace=True)
If you check documentation isnumeric is a method of Series.str and not of Series. That's why you get that error.
Also you will need the ==False because you have mixed types and get a series with only booleans.
I'm posting it in case this also helps you with your last question. You can use pandas.DataFrame.at with pandas.DataFrame.Itertuples for iteration over rows of the dataframe and replace values:
for row in df.itertuples():
# iterate every row and change the value of that column
if row.age == 'non_desirable_value:
df.at[row.Index, "age"] = 'desirable_value'
Hence, it could be:
for row in df.itertuples():
if row.age.str.isnumeric() == False or row.age == 'non_desirable_value':
df.at[row.Index, "age"] = 'desirable_value'
I have a multiindex DataFrame and I'm trying to select data in it base on certain criteria, so far so good. The problem is that once I have selected my data using .loc and pd.IndexSlice, the resulting DataFrame which should logically have less rows and less element in the first level of the multiindex keeps exactly the same multiIndex but with some keys in it refering to empty dataframe.
I've tried creating a completely new DataFrame with a new index, but the structure of my data set is complicating and there is not always the same number of elements in a given level, so it is not easy to created a dataFrame with the right shape in which I can put the data.
import numpy as np
import pandas as pd
np.random.seed(3) #so my exemple is reproductible
idx = pd.IndexSlice
iterables = [['A','B','C'],[0,1,2],['some','rdm','data']]
my_index = pd.MultiIndex.from_product(iterables,names =
['first','second','third'])
my_columns = ['col1','col2','col3']
df1 = pd.DataFrame(data = np.random.randint(10,size =
(len(my_index),len(my_columns))),
index = my_index,
columns = my_columns
)
#Ok, so let's say I want to keep only the elements in the first level of my index (["A","B","C"]) for
#which the total sum in column 3 is less than 35 for some reasons
boolean_mask = (df1.groupby(level = "first").col3.sum() < 35).tolist()
first_level_to_keep = df1.index.levels[0][boolean_mask].tolist()
#lets select the wanted data and put it in df2
df2 = df1.loc[idx[first_level_to_keep,:,:],:]
So far, everything is as expected
The problem is when I want to access the df2 index. I expected the following:
df2.index.levels[0].tolist() == ['B','C']
to be true. But this is what gives a True statement:
df2.index.levels[0].tolist() == ['A','B','C']
So my question is the following: is there a way to select data and to have in retrun a dataFrame with a multiindex reflecting what is in it. Because I find weird to be able to select non existing data in my df2:
I tried to put some images of the dataframes in question but I couldn't because I dont't have enough «reputation»... sorry about that.
Thank you for your time!
Even if you delete the rows corresponding to a particular value in an index level, that value still exists. You can reset the index and then set those columns back as an index in order to generate a MultiIndex with new level values.
df2 = df2.reset_index().set_index(['first','second','third'])
print(df2.index.levels[0].tolist() == ['B','C'])
True
I have 2 dataframes of numerical data. Given a value from one of the columns in the second df, I would like to look up the index for the value in the first df. More specifically, I would like to create a third df, which contains only index labels - using values from the second to look up its coordinates from the first.
listso = [[21,101],[22,110],[25,113],[24,112],[21,109],[28,108],[30,102],[26,106],[25,111],[24,110]]
data = pd.DataFrame(listso,index=list('abcdefghij'), columns=list('AB'))
rollmax = pd.DataFrame(data.rolling(center=False,window=5).max())
So for the third df, I hope to use the values from rollmax and figure out which row they showed up in data. We can call this third df indexlookup.
For example, rollmax.ix['j','A'] = 30, so indexlookup.ix['j','A'] = 'g'.
Thanks!
You can build a Series with the indexing the other way around:
mapA = pd.Series(data.index, index=data.A)
Then mapA[rollmax.ix['j','A']] gives 'g'.