I have a csv that I loaded into a Pandas Dataframe.
I then select only the rows with duplicate dates in the DF:
df_dups = df[df.duplicated(['Date'])].copy()
I'm trying to get the sum of all the rows with the exact same date for 4 columns (all float values), like this:
df_sum = df_dups.groupby('Date')["Received Quantity","Sent Quantity","Fee Amount","Market Value"].sum()
However, this does not give the desired result. When I examine df_sum.groups, I've noticed that it did not include the first date in the indices. So for two items with the same date, there would only be one index in the groups object.
pprint(df_dups.groupby('Date')["Received Quantity","Sent Quantity","Fee Amount","Market Value"].groups)
I have no idea how to get the sum of all duplicates.
I've also tried:
df_sum = df_dups.groupby('Date')["Received Quantity","Sent Quantity","Fee Amount","Market Value"].apply(lambda x : x.sum())
This gives the same result, which makes sense I guess, as the indices in the groupby object are not complete. What am I missing here?
Check the documentation for the method duplicated. By default duplicates are marked with True except for the first occurence, which is why the first date is not included in your sums.
You only need to pass in keep=False in duplicated for your desired behaviour.
df_dups = df[df.duplicated(['Date'], keep=False)].copy()
After that the sum can be calculated properly with the expression you wrote
df_sum = df_dups.groupby('Date')["Received Quantity","Sent Quantity","Fee Amount","Market Value"].apply(lambda x : x.sum())
Related
I've been working on a pandas DataFrame,
df = pd.DataFrame({'col':[-0.217514, -0.217834, 0.844116, 0.800125, 0.824554]}, index=[49082, 49083, 49853, 49854, 49855])
and I get data that looks like this:
As you can see, the index suddenly jumps 770 values (due to a sorting I did earlier).
Now I would like to split this DataFrame into many different ones, where each one would be made of the rows whose index follow each other only (here the first 2 rows would be in the same DataFrame while the last three would be in a different one).
Does anyone have an idea as to how to do this?
Thanks!
Use groupby on the index from which we subtract an increasing by 1 sequence, then stick each group as a separate df in the list
all_dfs = [g for _,g in df.groupby(df.index - np.arange(len(df.index)))]
all_dfs
output:
[ col
49082 -0.217514
49083 -0.217834,
col
49853 0.844116
49854 0.800125
49855 0.824554]
I've been trying to find out the top-3 highest frequency restaurant names under each type of restaurant
The columns are:
rest_type - Column for the type of restaurant
name - Column for the name of the restaurant
url - Column used for counting occurrences
This was the code that ended up working for me after some searching:
df_1=df.groupby(['rest_type','name']).agg('count')
datas=df_1.groupby(['rest_type'], as_index=False).apply(lambda x : x.sort_values(by="url",ascending=False).head(3))
['url'].reset_index().rename(columns={'url':'count'})
The final output was as follows:
I had a few questions pertaining to the above code:
How are we able to groupby using rest_type again for datas variable after grouping it earlier. Should it not give the missing column error? The second groupby operation is a bit confusing to me.
What does the first formulated column level_0 signify? I tried the code with as_index=True and it created an index and column pertaining to rest_type so I couldn't reset the index. Output below:
Thank you
You can use groupby a second time as it is present in the index which is recognized by groupby.
level_0 comes from the reset_index command because you index is unnamed.
That said, and provided I understand your dataset, I feel that you could achieve your goal more easily:
import random
df = pd.DataFrame({'rest_type': random.choices('ABCDEF', k=20),
'name': random.choices('abcdef', k=20),
'url': range(20), # looks like this is a unique identifier
})
def tops(s, n=3):
return s.value_counts().sort_values(ascending=False).head(n)
df.groupby('rest_type')['name'].apply(tops, n=3)
edit: here is an alternative to format the result as a dataframe with informative column names
(df.groupby('rest_type')
.apply(lambda x: x['name'].value_counts().nlargest(3))
.reset_index().rename(columns={'name': 'counts', 'level_1': 'name'})
)
I have a similar case where the above query looks working partially. In my case the cooccurrence value is coming as 1 always.
Here in my input data frame.
And my query is below
top_five_family_cooccurence_df = (common_top25_cooccurance1_df.groupby('family') .apply(lambda x: x['related_family'].value_counts().nlargest(5)) .reset_index().rename(columns={'related_family': 'cooccurence', 'level_1': 'related_family'}) )
I am getting result as
Where as The cooccurrence is always giving me 1.
I am trying to create a row at the bottom of a dataframe to show the sum of certain columns. I am under the impression that this shall be a really simple operation, but to my surprise, none of the methods I found on SO works for me in one step.
The methods that I've found on SO:
df.loc['TOTAL'] = df.sum()
This doesn't work for me as long as there are non-numeric columns in the dataframe. I need to select the columns first and then concat the non-numeric columns back
df.append(df.sum(numeric_only=True), ignore_index=True)
This won't preserve my data types. Integer column will be converted to float.
df3.loc['Total', 'ColumnA']= df['ColumnA'].sum()
I can only use this to sum one column.
I must have missed something in the process as this is not that hard an operation. Please let me know how I can add a sum row while preserving the data type of the dataframe.
Thanks.
Edit:
First off, sorry for the late update. I was on the road for the last weekend
Example:
df1 = pd.DataFrame(data = {'CountyID': [77, 95], 'Acronym': ['LC', 'NC'], 'Developable': [44490, 56261], 'Protected': [40355, 35943],
'Developed': [66806, 72211]}, index = ['Lehigh', 'Northampton'])
What I want to get would be
Please ignore the differences of the index.
It's a little tricky for me because I don't need to get the sum for the column 'County ID' since it's for specific indexing. So the question is more about getting the sum of specific numeric columns.
Thanks again.
Here is some toy data to use as an example:
df = pd.DataFrame({'A':[1.0,2.0,3.0],'B':[1,2,3],'C':['A','B','C']})
So that we can preserve the dtypes after the sum, we will store them as d
d = df.dtypes
Next, since we only want to sum numeric columns, pass numeric_only=True to sum(), but follow similar logic to your first attempt
df.loc['Total'] = df.sum(numeric_only=True)
And finally, reset the dtypes of your DataFrame to their original values.
df.astype(d)
A B C
0 1.0 1 A
1 2.0 2 B
2 3.0 3 C
Total 6.0 6 NaN
To select the numeric columns, you can do
df_numeric = df.select_dtypes(include = ['int64', 'float64'])
df_num_cols = df_numeric.columns
Then do what you did first (using what I found here)
df.loc['Total'] = pd.Series(df[df_num_cols].sum(), index = [df_num_cols])
I have two dataframes. The first named mergedcsv is of the format:
mergedcsv dataframe
The second dataframe named idgrp_df is of a dictionary format which for each region Id a list of corresponding string ids.
idgrp_df dataframe - keys with lists
For each row in mergedcsv (and the corresponding row in idgrp_df) I wish to select the columns within mergedcsv where the column labels are equal to the list with idgrp_df for that row. Then sum the values of those particular values and add the output to a column within mergedcsv. The function will iterate through all rows in mergedcsv (582 rows x 600 columns).
My line of code to try to attempt this is:
mergedcsv['TotRegFlows'] = mergedcsv.groupby([idgrp_df],as_index=False).numbers.apply(lambda x: x.iat[0].sum())
It returns a ValueError: Grouper for class pandas.core.frame.DataFrame not 1-dimensional.
This relates to the input dataframe for the groupby. How can I access the list for each row as the input for the groupby?
So for example, for the first row in mergedcsv I wish to select the columns with labels F95RR04, F95RR06 and F95RR15 (reading from the list in the first row of idgrp_df). Sum the values in these columns for that row and insert the sum value into TotRegFlows column.
Any ideas as to how I can utilize the list would be very much appreciated.
Edits:
Many thanks IanS. Your solution is useful. Following modification of the code line based on this advice I realised that (as suggested) my index in both dataframes are out of sync. I tested the indices (mergedcsv had 'None' and idgrp_df has 'REG_ID' column as index. I set the mergedcsv to 'REG_ID' also. Then realised that the mergedcsv has 582 rows (the REG_ID is not unique) and the idgrp_df has 220 rows (REG_ID is unique). I therefor think I am missing a groupby based on REG_ID index in mergedcsv.
I have modified the code as follows:
mergedcsv.set_index('REG_ID', inplace=True)
print mergedcsv.index.name
print idgrp_df.index.name
mergedcsvgroup = mergedcsv.groupby('REG_ID')[mergedcsv.columns].apply(lambda y: y.tolist())
mergedcsvgroup['TotRegFlows'] = mergedcsvgroup.apply(lambda row: row[idgrp_df.loc[row.name]].sum(), axis=1)
I have a keyError:'REG_ID'.
Any further recommendations are most welcome. Would it be more efficient to combine the groupby and apply into one line?
I am new to working with pandas and trying to build experience in python
Further amendments:
Without an index for mergedcsv:
mergedcsv['TotRegFlows'] = mergedcsv.apply(lambda row: row[idgrp_df.loc[row.name]].groupby('REG_ID').sum(), axis=1)
this throws a KeyError: (the label[0] is not in the [index], u 'occurred at index 0')
With an index for mergedcsv:
mergedcsv.set_index('REG_ID', inplace=True)
columnlist = list(mergedcsv.columns.values)
mergedcsv['TotRegFlows'] = mergedcsv.apply(lambda row: row[idgrp_df.loc[row.name]].groupby('REG_ID')[columnlist].transform().sum(), axis=1)
this throws a TypeError: ("unhashable type:'list'", u'occurred at index 7')
Or finally separating the groupby function:
columnlist = list(mergedcsv.columns.values)
mergedcsvgroup = mergedcsv.groupby('REG_ID')
mergedcsv['TotRegFlows'] = mergedcsvgroup.apply(lambda row: row[idgrp_df.loc[row.name]].sum())
this throws a TypeError: unhashable type list. The axis=1 argument is not available also with groupby apply.
Any ideas how I can use the lists with the apply function? I've explored tuples in the apply code but have not had any success.
Any suggestions much appreciated.
If I understand correctly, I have a simple solution with apply:
Setup
import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6], 'C': [7,8,9]})
lists = pd.Series([['A', 'B'], ['A', 'C'], ['C']])
Solution
I apply a lambda function that gets the list of columns to be summed from the lists series:
df.apply(lambda row: row[lists[row.name]].sum(), axis=1)
The trick is that, when iterating over rows (axis=1), row.name is the original index of the dataframe df. I use that to access the list from the lists series.
Notes
This solution assumes that both dataframes share the same index, which appears not to be the case in the screenshots you included. You have to address that.
Also, if idgrp_df is a dataframe and not a series, then you need to access its values with .loc.
city state neighborhoods categories
Dravosburg PA [asas,dfd] ['Nightlife']
Dravosburg PA [adad] ['Auto_Repair','Automotive']
I have above dataframe I want to convert each element of a list into column for eg:
city state asas dfd adad Nightlife Auto_Repair Automotive
Dravosburg PA 1 1 0 1 1 0
I am using following code to do this :
def list2columns(df):
"""
to convert list in the columns
of a dataframe
"""
columns=['categories','neighborhoods']
for col in columns:
for i in range(len(df)):
for element in eval(df.loc[i,"categories"]):
if len(element)!=0:
if element not in df.columns:
df.loc[:,element]=0
else:
df.loc[i,element]=1
How to do this in more efficient way?
Why still there is below warning when I am using df.loc already
SettingWithCopyWarning: A value is trying to be set on a copy of a slice
from a DataFrame.Try using .loc[row_indexer,col_indexer] = value instead
Since you're using eval(), I assume each column has a string representation of a list, rather than a list itself. Also, unlike your example above, I'm assuming there are quotes around the items in the lists in your neighborhoods column (df.iloc[0, 'neighborhoods'] == "['asas','dfd']"), because otherwise your eval() would fail.
If this is all correct, you could try something like this:
def list2columns(df):
"""
to convert list in the columns of a dataframe
"""
columns = ['categories','neighborhoods']
new_cols = set() # list of all new columns added
for col in columns:
for i in range(len(df[col])):
# get the list of columns to set
set_cols = eval(df.iloc[i, col])
# set the values of these columns to 1 in the current row
# (if this causes new columns to be added, other rows will get nans)
df.iloc[i, set_cols] = 1
# remember which new columns have been added
new_cols.update(set_cols)
# convert any un-set values in the new columns to 0
df[list(new_cols)].fillna(value=0, inplace=True)
# if that doesn't work, this may:
# df.update(df[list(new_cols)].fillna(value=0))
I can only speculate on an answer to your second question, about the SettingWithCopy warning.
It's possible (but unlikely) that using df.iloc instead of df.loc will help, since that is intended to select by row number (in your case, df.loc[i, col] only works because you haven't set an index, so pandas uses the default index, which matches the row number).
Another possibility is that the df that is passed in to your function is already a slice from a larger dataframe, and that is causing the SettingWithCopy warning.
I've also found that using df.loc with mixed indexing modes (logical selectors for rows and column names for columns) produces the SettingWithCopy warning; it's possible that your slice selectors are causing similar problems.
Hopefully the simpler and more direct indexing in the code above will solve any of these problems. But please report back (and provide code to generate df) if you are still seeing that warning.
Use this instead
def list2columns(df):
"""
to convert list in the columns
of a dataframe
"""
df = df.copy()
columns=['categories','neighborhoods']
for col in columns:
for i in range(len(df)):
for element in eval(df.loc[i,"categories"]):
if len(element)!=0:
if element not in df.columns:
df.loc[:,element]=0
else:
df.loc[i,element]=1
return df