What seems to be a simple function returns NaNs instead of the actual numbers. What am I missing here?
#Concatenate the dataframes:
dfcal = dfcal.astype(float)
dfmag = dfmag.astype(float)
print('dfcal\n-----',dfcal)
print('dfmag\n-----',dfmag)
df = pd.concat([dfcal,dfmag])
print('concatresult\n-----',df)
Cheers!
I guess you need axis=1 for append new columns, selected column caliper for avoid duplicated depth columns:
df = pd.concat([dfcal['caliper'],dfmag], axis=1)
Or:
df = pd.concat([dfcal.drop('depth', axis=1),dfmag], axis=1)
Check parameters (join, axis) or use merge
join{‘inner’, ‘outer’}, default ‘outer’
df = pd.concat([dfcal,dfmag], join='inner')
Related
I was trying to add a new Column to my dataset but when i did the column only had 1 index
is there a way to make one value be in al indexes in a column
import pandas as pd
df = pd.read_json('file_1.json', lines=True)
df2 = pd.read_json('file_2.json', lines=True)
df3 = pd.concat([df,df2])
df3 = df.loc[:, ['renderedContent']]
görüş_column = ['Milet İttifakı']
df3['Siyasi Yönelim'] = görüş_column
As per my understanding, this could be your possible solution:-
You have mentioned these lines of code:-
df3 = pd.concat([df,df2])
df3 = df.loc[:, ['renderedContent']]
You can modify them into
df3 = pd.concat([df,df2],axis=1) ## axis=1 means second dataframe will add to columns, default value is axis=0 which adds to the rows
Second point is,
df3 = df3.loc[:, ['renderedContent']]
I think you want to write this one , instead of df3=df.loc[:,['renderedContent']].
Hope it will solve your problem.
I have a initial dataframe D. I extract two data frames from it like this:
A = D[D.label == k]
B = D[D.label != k]
I want to combine A and B into one DataFrame. The order of the data is not important. However, when we sample A and B from D, they retain their indexes from D.
DEPRECATED: DataFrame.append and Series.append were deprecated in v1.4.0.
Use append:
df_merged = df1.append(df2, ignore_index=True)
And to keep their indexes, set ignore_index=False.
Use pd.concat to join multiple dataframes:
df_merged = pd.concat([df1, df2], ignore_index=True, sort=False)
Merge across rows:
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
Merge across columns:
df_col_merged = pd.concat([df_a, df_b], axis=1)
If you're working with big data and need to concatenate multiple datasets calling concat many times can get performance-intensive.
If you don't want to create a new df each time, you can instead aggregate the changes and call concat only once:
frames = [df_A, df_B] # Or perform operations on the DFs
result = pd.concat(frames)
This is pointed out in the pandas docs under concatenating objects at the bottom of the section):
Note: It is worth noting however, that concat (and therefore append)
makes a full copy of the data, and that constantly reusing this
function can create a significant performance hit. If you need to use
the operation over several datasets, use a list comprehension.
If you want to update/replace the values of first dataframe df1 with the values of second dataframe df2. you can do it by following steps —
Step 1: Set index of the first dataframe (df1)
df1.set_index('id')
Step 2: Set index of the second dataframe (df2)
df2.set_index('id')
and finally update the dataframe using the following snippet —
df1.update(df2)
To join 2 pandas dataframes by column, using their indices as the join key, you can do this:
both = a.join(b)
And if you want to join multiple DataFrames, Series, or a mixture of them, by their index, just put them in a list, e.g.,:
everything = a.join([b, c, d])
See the pandas docs for DataFrame.join().
# collect excel content into list of dataframes
data = []
for excel_file in excel_files:
data.append(pd.read_excel(excel_file, engine="openpyxl"))
# concatenate dataframes horizontally
df = pd.concat(data, axis=1)
# save combined data to excel
df.to_excel(excelAutoNamed, index=False)
You can try the above when you are appending horizontally! Hope this helps sum1
Use this code to attach two Pandas Data Frames horizontally:
df3 = pd.concat([df1, df2],axis=1, ignore_index=True, sort=False)
You must specify around what axis you intend to merge two frames.
I am trying to explode a list in my dataframe column and merge it back to the df, but i get a memory error while merging the flatten column with the initial dataframe. I would like to know if i can merge it in chunks, so that i can overcome the memory issue.
def flatten_colum_with_list(df, column, reset_index=False):
column_to_flatten = pd.DataFrame([[i, x] for i, y in df[column].apply(list).iteritems() for x in y], columns=['I', column])
column_to_flatten = column_to_flatten.set_index('I')
df = df.drop(column, axis=1)
df = df.merge(column_to_flatten, left_index=True, right_index=True)
if reset_index:
df = df.reset_index(drop=True)
return df
I would appreciate any support.
Regarding this, you can simply use the following code:
df.explode(*column name here*,ignore_index=True)
The ignore_index set to true will set the index to 0,1,2,....... order.
I have the following toy code:
import pandas as pd
df = pd.DataFrame()
df["foo"] = [1,2,3,4]
df2 = pd.DataFrame()
df2["bar"]=[4,5,6,7]
df = pd.concat([df,df2], ignore_index=True,axis=1)
print(list(df))
Output: [0,1]
Expected Output: [foo,bar] (order is not important)
Is there any way to concatenate two dataframes without losing the original column headers, if I can guarantee that the headers will be unique?
Iterating through the columns and then adding them to one of the DataFrames comes to mind, but is there a pandas function, or concat parameter that I am unaware of?
Thanks!
As stated in merge, join, and concat documentation, ignore index will remove all name references and use a range (0...n-1) instead. So it should give you the result you want once you remove ignore_index argument or set it to false (default).
df = pd.concat([df, df2], axis=1)
This will join your df and df2 based on indexes (same indexed rows will be concatenated, if other dataframe has no member of that index it will be concatenated as nan).
If you have different indexing on your dataframes, and want to concatenate it this way. You can either create a temporary index and join on that, or set the new dataframe's columns after using concat(..., ignore_index=True).
I don't think the accepted answer answers the question, which is about column headers, not indexes.
I am facing the same problem, and my workaround is to add the column names after the concatenation:
df.columns = ["foo", "bar"]
I have a initial dataframe D. I extract two data frames from it like this:
A = D[D.label == k]
B = D[D.label != k]
I want to combine A and B into one DataFrame. The order of the data is not important. However, when we sample A and B from D, they retain their indexes from D.
DEPRECATED: DataFrame.append and Series.append were deprecated in v1.4.0.
Use append:
df_merged = df1.append(df2, ignore_index=True)
And to keep their indexes, set ignore_index=False.
Use pd.concat to join multiple dataframes:
df_merged = pd.concat([df1, df2], ignore_index=True, sort=False)
Merge across rows:
df_row_merged = pd.concat([df_a, df_b], ignore_index=True)
Merge across columns:
df_col_merged = pd.concat([df_a, df_b], axis=1)
If you're working with big data and need to concatenate multiple datasets calling concat many times can get performance-intensive.
If you don't want to create a new df each time, you can instead aggregate the changes and call concat only once:
frames = [df_A, df_B] # Or perform operations on the DFs
result = pd.concat(frames)
This is pointed out in the pandas docs under concatenating objects at the bottom of the section):
Note: It is worth noting however, that concat (and therefore append)
makes a full copy of the data, and that constantly reusing this
function can create a significant performance hit. If you need to use
the operation over several datasets, use a list comprehension.
If you want to update/replace the values of first dataframe df1 with the values of second dataframe df2. you can do it by following steps —
Step 1: Set index of the first dataframe (df1)
df1.set_index('id')
Step 2: Set index of the second dataframe (df2)
df2.set_index('id')
and finally update the dataframe using the following snippet —
df1.update(df2)
To join 2 pandas dataframes by column, using their indices as the join key, you can do this:
both = a.join(b)
And if you want to join multiple DataFrames, Series, or a mixture of them, by their index, just put them in a list, e.g.,:
everything = a.join([b, c, d])
See the pandas docs for DataFrame.join().
# collect excel content into list of dataframes
data = []
for excel_file in excel_files:
data.append(pd.read_excel(excel_file, engine="openpyxl"))
# concatenate dataframes horizontally
df = pd.concat(data, axis=1)
# save combined data to excel
df.to_excel(excelAutoNamed, index=False)
You can try the above when you are appending horizontally! Hope this helps sum1
Use this code to attach two Pandas Data Frames horizontally:
df3 = pd.concat([df1, df2],axis=1, ignore_index=True, sort=False)
You must specify around what axis you intend to merge two frames.