Is it possible to append Series to rows of DataFrame without making a list first?

Question

I have some data I\'m trying to organize into a DataFrame in Pandas. I was trying to make each row a Series and append it to the Da

Answer 1

Try using this command. See the example given below:

df.loc[len(df)] = ['Product 9',99,9.99,8.88,1.11]

df

Answer 2

DataFrame.append does not modify the DataFrame in place. You need to do df = df.append(...) if you want to reassign it back to the original variable.

Answer 3

Convert the series to a dataframe and transpose it, then append normally.

srs = srs.to_frame().T
df = df.append(srs)

Answer 4

This would work as well:

df = pd.DataFrame()
new_line = pd.Series({'A2M': 4.059, 'A2ML1': 4.28}, name='HCC1419')
df = df.append(new_line, ignore_index=False)

The name in the Series will be the index in the dataframe. ignore_index=False is the important flag in this case.

Answer 5

Something like this could work...

mydf.loc['newindex'] = myseries

Here is an example where I used it...

stats = df[['bp_prob', 'ICD9_prob', 'meds_prob', 'regex_prob']].describe()

stats
Out[32]: 
          bp_prob   ICD9_prob   meds_prob  regex_prob
count  171.000000  171.000000  171.000000  171.000000
mean     0.179946    0.059071    0.067020    0.126812
std      0.271546    0.142681    0.152560    0.207014
min      0.000000    0.000000    0.000000    0.000000
25%      0.000000    0.000000    0.000000    0.000000
50%      0.000000    0.000000    0.000000    0.013116
75%      0.309019    0.065248    0.066667    0.192954
max      1.000000    1.000000    1.000000    1.000000

medians = df[['bp_prob', 'ICD9_prob', 'meds_prob', 'regex_prob']].median()

stats.loc['median'] = medians

stats
Out[36]: 
           bp_prob   ICD9_prob   meds_prob  regex_prob
count   171.000000  171.000000  171.000000  171.000000
mean      0.179946    0.059071    0.067020    0.126812
std       0.271546    0.142681    0.152560    0.207014
min       0.000000    0.000000    0.000000    0.000000
25%       0.000000    0.000000    0.000000    0.000000
50%       0.000000    0.000000    0.000000    0.013116
75%       0.309019    0.065248    0.066667    0.192954
max       1.000000    1.000000    1.000000    1.000000
median    0.000000    0.000000    0.000000    0.013116

Answer 6

Maybe an easier way would be to add the pandas.Series into the pandas.DataFrame with ignore_index=True argument to DataFrame.append(). Example -

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value)
    DF = DF.append(SR_row,ignore_index=True)

Demo -

In [1]: import pandas as pd

In [2]: df = pd.DataFrame([[1,2],[3,4]],columns=['A','B'])

In [3]: df
Out[3]:
   A  B
0  1  2
1  3  4

In [5]: s = pd.Series([5,6],index=['A','B'])

In [6]: s
Out[6]:
A    5
B    6
dtype: int64

In [36]: df.append(s,ignore_index=True)
Out[36]:
   A  B
0  1  2
1  3  4
2  5  6

---

Another issue in your code is that DataFrame.append() is not in-place, it returns the appended dataframe, you would need to assign it back to your original dataframe for it to work. Example -

DF = DF.append(SR_row,ignore_index=True)

---

To preserve the labels, you can use your solution to include name for the series along with assigning the appended DataFrame back to DF. Example -

DF = DataFrame()
for sample,data in D_sample_data.items():
    SR_row = pd.Series(data.D_key_value,name=sample)
    DF = DF.append(SR_row)
DF.head()