I have a pandas dataframe like following when I write this dataframe into google sheets I found out header is missing. My questions is how to make it work? or copy columns name into first row of data and other data does not change?
import pandas as pd
year = [2005, 2006, 2007]
A = [4, 5, 7]
B = [3, 3, 9]
C = [1, 7, 6]
df_old = pd.DataFrame({'year' : year, 'A' : A, 'B' : B, 'C' : C}, columns=['year', 'A', 'B', 'c'])
Out[25]:
A B C year
0 4 3 1 2005
1 5 3 7 2006
2 7 9 6 2007
#want output
Out[25]:
A B C year
0 A B C year
1 4 3 1 2005
2 5 3 7 2006
3 7 9 6 2007
You can also check the following answer: https://stackoverflow.com/a/24284680/11127365
For your case, the first line will be df_old.loc[-1] = df_old.columns
IIUC
df=pd.DataFrame(df_old.columns.values[None,:],columns=df_old.columns).\
append(df_old).\
reset_index(drop=True)
df
year A B c
0 year A B c
1 2005 4 3 NaN
2 2006 5 3 NaN
3 2007 7 9 NaN
Related
This question already has answers here:
How can I replicate rows of a Pandas DataFrame?
(10 answers)
Closed 11 months ago.
I want to replicate rows in a Pandas Dataframe. Each row should be repeated n times, where n is a field of each row.
import pandas as pd
what_i_have = pd.DataFrame(data={
'id': ['A', 'B', 'C'],
'n' : [ 1, 2, 3],
'v' : [ 10, 13, 8]
})
what_i_want = pd.DataFrame(data={
'id': ['A', 'B', 'B', 'C', 'C', 'C'],
'v' : [ 10, 13, 13, 8, 8, 8]
})
Is this possible?
You can use Index.repeat to get repeated index values based on the column then select from the DataFrame:
df2 = df.loc[df.index.repeat(df.n)]
id n v
0 A 1 10
1 B 2 13
1 B 2 13
2 C 3 8
2 C 3 8
2 C 3 8
Or you could use np.repeat to get the repeated indices and then use that to index into the frame:
df2 = df.loc[np.repeat(df.index.values, df.n)]
id n v
0 A 1 10
1 B 2 13
1 B 2 13
2 C 3 8
2 C 3 8
2 C 3 8
After which there's only a bit of cleaning up to do:
df2 = df2.drop("n", axis=1).reset_index(drop=True)
id v
0 A 10
1 B 13
2 B 13
3 C 8
4 C 8
5 C 8
Note that if you might have duplicate indices to worry about, you could use .iloc instead:
df.iloc[np.repeat(np.arange(len(df)), df["n"])].drop("n", axis=1).reset_index(drop=True)
id v
0 A 10
1 B 13
2 B 13
3 C 8
4 C 8
5 C 8
which uses the positions, and not the index labels.
You could use set_index and repeat
In [1057]: df.set_index(['id'])['v'].repeat(df['n']).reset_index()
Out[1057]:
id v
0 A 10
1 B 13
2 B 13
3 C 8
4 C 8
5 C 8
Details
In [1058]: df
Out[1058]:
id n v
0 A 1 10
1 B 2 13
2 C 3 8
It's something like the uncount in tidyr:
https://tidyr.tidyverse.org/reference/uncount.html
I wrote a package (https://github.com/pwwang/datar) that implements this API:
from datar import f
from datar.tibble import tribble
from datar.tidyr import uncount
what_i_have = tribble(
f.id, f.n, f.v,
'A', 1, 10,
'B', 2, 13,
'C', 3, 8
)
what_i_have >> uncount(f.n)
Output:
id v
0 A 10
1 B 13
1 B 13
2 C 8
2 C 8
2 C 8
Not the best solution, but I want to share this: you could also use pandas.reindex() and .repeat():
df.reindex(df.index.repeat(df.n)).drop('n', axis=1)
Output:
id v
0 A 10
1 B 13
1 B 13
2 C 8
2 C 8
2 C 8
You can further append .reset_index(drop=True) to reset the .index.
import pandas as pd
df1 = pd.DataFrame({"A":[14, 4, 5, 4],"B":[1,2,3,4]})
df2 = pd.DataFrame({"A":[14, 4, 5, 4],"C":[5,6,7,8]})
df = pd.concat([df1,df2],axis=1)
Let's see the concated df,the first column and third column shares the same column name A.
df
A B A C
0 14 1 14 5
1 4 2 4 6
2 5 3 5 7
3 4 4 4 8
I want to get the following format.
df
A B C
0 14 1 5
1 4 2 6
2 5 3 7
3 4 4 8
Drop column by id.
result = df.drop(df.columns[2],axis=1)
result
B C
0 1 5
1 2 6
2 3 7
3 4 8
I can get what i expect this way:
import pandas as pd
df1 = pd.DataFrame({"A":[14, 4, 5, 4],"B":[1,2,3,4]})
df2 = pd.DataFrame({"A":[14, 4, 5, 4],"C":[5,6,7,8]})
df2 = df2.drop(df2.columns[0],axis=1)
df = pd.concat([df1,df2],axis=1)
It is so strange that both the first and third column removed when to drop specified column by id.
1.Please tell me the reason of dataframe's this action.
2.How can i remove the third column at the same time keep the first column undeleted?
Here's a way using indexes:
index_to_drop = 2
# get indexes to keep
col_idxs = [en for en, _ in enumerate(df.columns) if en != index_to_drop]
# subset the df
df = df.iloc[:,col_idxs]
A B C
0 14 1 5
1 4 2 6
2 5 3 7
3 4 4 8
I have a dataframe like below:
>>> df1
a b
0 [1, 2, 3] 10
1 [4, 5, 6] 20
2 [7, 8] 30
and another like:
>>> df2
a
0 1
1 2
2 3
3 4
4 5
I need to create column 'c' in df2 from column 'b' of df1 if column 'a' value of df2 is in coulmn 'a' df1. In df1 each tuple of column 'a' is a list.
I have tried to implement from following url, but got nothing so far:
https://medium.com/#Imaadmkhan1/using-pandas-to-create-a-conditional-column-by-selecting-multiple-columns-in-two-different-b50886fabb7d
expect result is
>>> df2
a c
0 1 10
1 2 10
2 3 10
3 4 20
4 5 20
Use Series.map by flattening values from df1 to dictionary:
d = {c: b for a, b in zip(df1['a'], df1['b']) for c in a}
print (d)
{1: 10, 2: 10, 3: 10, 4: 20, 5: 20, 6: 20, 7: 30, 8: 30}
df2['new'] = df2['a'].map(d)
print (df2)
a new
0 1 10
1 2 10
2 3 10
3 4 20
4 5 20
EDIT: I think problem is mixed integers in list in column a, solution is use if/else for test it for new dictionary:
d = {}
for a, b in zip(df1['a'], df1['b']):
if isinstance(a, list):
for c in a:
d[c] = b
else:
d[a] = b
df2['new'] = df2['a'].map(d)
Use :
m=pd.DataFrame({'a':np.concatenate(df.a.values),'b':df.b.repeat(df.a.str.len())})
df2.merge(m,on='a')
a b
0 1 10
1 2 10
2 3 10
3 4 20
4 5 20
First we unnest the list df1 to rows, then we merge them on column a:
df1 = df1.set_index('b').a.apply(pd.Series).stack().reset_index(level=0).rename(columns={0:'a'})
print(df1, '\n')
df_final = df2.merge(df1, on='a')
print(df_final)
b a
0 10 1.0
1 10 2.0
2 10 3.0
0 20 4.0
1 20 5.0
2 20 6.0
0 30 7.0
1 30 8.0
a b
0 1 10
1 2 10
2 3 10
3 4 20
4 5 20
I have df that looks like this:
a b c d e f
1 na 2 3 4 5
1 na 2 3 4 5
1 na 2 3 4 5
1 6 2 3 4 5
How do I trim and reshape the dataframe so that for every column the n/a are dropped and the dataframe looks like this:
Edit;
df.dropna() is dropping all the rows.
a b c d e f
1 6 2 3 4 5
This dataframe has millions of rows, I need to be able to drop the n/a rows by column while retaining rows and columns with data in them.
edit;
df.dropna() is dropping all the rows in the column. When I check if the columns with n/a are empty, df.column_name.empty() I get false. So there is data in columns with n/a
For me dropna working nice for remove missing values and Nones:
df = df.dropna()
print (df)
a b c d e f
3 1 6.0 2 3 4 5
But if possible multiple values for removing create mask by isin, chain testing missing values with isnull and last filter by any - return at least one True per row by inverted mask ~:
df = pd.DataFrame({'a': ['a', None, 's', 'd'],
'b': ['na',7, 2, 6],
'c': [2, 2, 2, 2],
'd': [3, 3, 3, 3],
'e': [4, 4, np.nan, 4],
'f': [5, 5, 5, 5]})
print (df)
a b c d e f
0 a na 2 3 4.0 5
1 None 7 2 3 4.0 5
2 s 2 2 3 NaN 5
3 d 6 2 3 4.0 5
df1 = df.dropna()
print (df1)
a b c d e f
0 a na 2 3 4.0 5
3 d 6 2 3 4.0 5
mask = (df.isin(['na', 'n/a']) | df.isnull()).any(axis=1)
df2 = df[~mask]
print (df2)
a b c d e f
3 d 6 2 3 4.0 5
I'm trying to combine multiple data frames in pandas and I want the new dataframe to contain the maximum element within the various dataframes. All of the dataframes have the same row and column labels. How can I do this?
Example:
df1 = Date A B C
1/1/15 3 5 1
2/1/15 2 4 7
df2 = Date A B C
1/1/15 7 2 2
2/1/15 1 5 4
I'd like the result to look like this.
df = Date A B C
1/1/15 7 5 2
2/1/15 2 5 7
You can use np.where to return an array of the values that satisfy your boolean condition, this can then be used to construct a df:
In [5]:
vals = np.where(df1 > df2, df1, df2)
vals
Out[5]:
array([['1/1/15', 7, 5, 2],
['2/1/15', 2, 5, 7]], dtype=object)
In [6]:
pd.DataFrame(vals, columns = df1.columns)
Out[6]:
Date A B C
0 1/1/15 7 5 2
1 2/1/15 2 5 7
I don't know if Date is a column or index but the end result will be the same.
EDIT
Actually just use np.maximum:
In [8]:
np.maximum(df1,df2)
Out[8]:
Date A B C
0 1/1/15 7 5 2
1 2/1/15 2 5 7