I am trying to merge those two dataframes in order to replace in the left one values that are present in the right one with the same ticker and datetime.
Here is a small example
Here's a way using update:
# update uses index matching
left_df = left_df.set_index('Timestamp')
right_df = right_df.set_index('Timestamp')
# update does inplace modification, so returns nothing.
left_df.update(right_df)
print(left_df)
Related
I'm trying to combine two dataframes together in pandas using left merge on common columns, only when I do that the data that I merged doesn't carry over and instead gives NaN values. All of the columns are objects and match that way, so i'm not quite sure whats going on.
this is my first dateframe header, which is the output from a program
this is my second data frame header. the second df is a 'key' document to match the first output with its correct id/tastant/etc and they share the same date/subject/procedure/etc
and this is my code thats trying to merge them on the common columns.
combined = first.merge(second, on=['trial', 'experiment','subject', 'date', 'procedure'], how='left')
with output (the id, ts and tastant columns should match correctly with the first dataframe but doesn't.
Check your dtypes, make sure they match between the 2 dataframes. Pandas makes assumptions about data types when it imports, it could be assuming numbers are int in one dataframe and object in another.
For the string columns, check for additional whitespaces. They can appear in datasets and since you can't see them and Pandas can, it result in no match. You can use df['column'].str.strip().
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.strip.html
I have two data frames that I imported as spreadsheets into Pandas and cleaned up. They have a similar key value called 'PurchaseOrders' that I am using to match product numbers to a shipment number. When I attempt to merge them, I only end up with a df of 34 rows, but I have over 400 pairs of matching product to shipment numbers.
This is the closest I've gotten, but I have also tried using join()
ShipSheet = pd.merge(new_df, orders, how ='inner')
ShipSheet.shape
Here is my order df
orders df
and here is my new_df that I want to add to my orders df using the 'PurchaseOrders' key
new_df
In the end, I want them to look like this
end goal df
I am not sure if I'm not using the merge function improperly, but my end product should have around 300+ rows. I will note that the new_df data frame's 'PurchaseOrders' values had to be delimited from a single column and split into rows, so I guess this could have something to do with it.
Use the merge method on the dataframe and specify the key
merged_inner = pd.merge(left=df_left, right=df_right, left_on='PurchaseOrders', right_on='PurchaseOrders')
learn more here
Use the concat method on pandas and specify the axis.
final_df = pd.concat([new_df, order], axis = 1)
when you specify the axis please careful if you specify axis = 0 then it placed second data frame under the first one and if you specify axis = 1 then it placed the second data frame right to the first data frame.
So I have 2 tables, Table 1 and Table 2, Table 2 is sorted with the dates- recent dates to old dates. So in excel when I do a lookup in Table 1 and the lookup is done from Table 2, It only picks the first value from table 2 and does not move on to search for the same value after the first.
So I tried replicating it in python with the merge function, but found out it gets to repeat the value the number of times it appears in the second table.
pd.merge(Table1, Table2, left_on='Country', right_on='Country', how='left', indicator='indicator_column')
TABLE1
TABLE2
Merger result
Expected Result(Excel vlookup)
Is there any way this could be achieved with the merge function or any other python function?
Typing this in the blind as you are including your data as images, not text.
# The index is a very important element in a DataFrame
# We will see that in a bit
result = table1.set_index('Country')
# For each country, only keep the first row
tmp = table2.drop_duplicates(subset='Country').set_index('Country')
# When you assign one or more columns of a DataFrame to one or more columns of
# another DataFrame, the assignment is aligned based on the index of the two
# frames. This is the equivalence of VLOOKUP
result.loc[:, ['Age', 'Date']] = tmp[['Age', 'Date']]
result.reset_index(inplace=True)
Edit: Since you want a straight up Vlookup, just use join. It appears to find the very first one.
table1.join(table2, rsuffix='r', lsuffix='l')
The docs seem to indicate it performs similarly to a vlookup: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html
I'd recommend approaching this more like a SQL join than a Vlookup. Vlookup finds the first matching row, from top to bottom, which could be completely arbitrary depending on how you sort your table/array in excel. "True" database systems and their related functions are more detailed than this, for good reason.
In order to join only one row from the right table onto one row of the left table, you'll need some kind of aggregation or selection - So in your case, that'd be either MAX or MIN.
The question is, which column is more important? The date or age?
import pandas as pd
df1 = pd.DataFrame({
'Country':['GERM','LIB','ARG','BNG','LITH','GHAN'],
'Name':['Dave','Mike','Pete','Shirval','Kwasi','Delali']
})
df2 = pd.DataFrame({
'Country':['GERM','LIB','ARG','BNG','LITH','GHAN','LIB','ARG','BNG'],
'Age':[35,40,27,87,90,30,61,18,45],
'Date':['7/10/2020','7/9/2020','7/8/2020','7/7/2020','7/6/2020','7/5/2020','7/4/2020','7/3/2020','7/2/2020']
})
df1.set_index('Country')\
.join(
df2.groupby('Country')\
.agg({'Age':'max','Date':'max'}), how='left', lsuffix='l', rsuffix='r')
I am trying to find columns hitting specific conditions and put a value in the column col.
My current implementation is:
df.loc[~(df['myCol'].isin(myInfo)), 'col'] = 'ok'
In the future, myCol will have multiple info. So I need to split the value in myCol without changing the dataframe and check if any of the splitted values are in myInfo. If one of them are, the current row should get the value 'ok' in the column col. Is there an elegant way without really splitting and saving in an extra variable?
Currently, I do not know how the multiple info will be represented (either separated by a character or just concatenated one after one, each consisting of 4 alphanumeric values).
Let's say you need to split on "-" for your myCol column.
sep='-'
deconcat = df['MyCol'].str.split(sep, expand=True)
new_df=df.join(deconcat)
The new_df DataFrame will have the same index as df, therefore you can do what you want with new_df and then join back to df to filter it how you want.
You can do the above .isin code for each of the new split columns to get your desired result.
Source:
Code taken from the pyjanitor documentation which has a built-in function, deconcatenate_column, that does this.
Source code for deconcatenate_column
I'm stuck on particluar python question here. I have 2 dataframes DF1 and DF2. In both, I have 2 columns pID and yID (which are not indexed, just default). I'm look to add a column Found in DF1 where the respective values of columns (pID and yID) were found in DF2. Also, I would like to zone in on just values in DF2 where aID == 'Text'.
I believe the below gets me the 1st part of this question; however, I'm unsure how as to incorporate the where.
DF1['Found'] = (DF1[['pID', 'yID']] == DF2[['pID','yID']]).all(axis=1).astype(bool)
Suggestions or answers would be most appreciated. Thanks.
You could subset the second dataframe containing aID == 'Text' to get a reduced DF from which select those portions of columns to be compared against the first dataframe.
Use DF.isin() to check if the values that are present under these column names match or not. And, .all(axis=1) returns True if both the columns happen to be True, else they become False. Convert the boolean series to integers via astype(int) and assign the result to the new column, Found.
df1_sub = df1[['pID', 'yID']]
df2_sub = df2.query('aID=="Text"')[['pID', 'yID']]
df1['Found'] = df1_sub.isin(df2_sub).all(axis=1).astype(int)
df1
Demo DF's used:
df1 = pd.DataFrame(dict(pID=[1,2,3,4,5],
yID=[10,20,30,40,50]))
df2 = pd.DataFrame(dict(pID=[1,2,8,4,5],
yID=[10,12,30,40,50],
aID=['Text','Best','Text','Best','Text']))
If it does not matter where those matches occur, then merge the two dataframes on 'pID', 'yID' common columns as the key by considering the bigger DF's index (right_index=True) as the new index axis that needs to be emitted and aligned after the merge operation is over.
Access these indices which indicate matches found and assign the value, 1 to a new column named Found while filling it's missing elements with 0's throughout.
df1.loc[pd.merge(df1_sub, df2_sub, on=['pID', 'yID'], right_index=True).index, 'Found'] = 1
df1['Found'].fillna(0, inplace=True)
df1 should be modifed accordingly post the above steps.