Append rows to a pandas DataFrame without making a new copy

Append rows to a pandas DataFrame without making a new copy

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Append rows to a pandas DataFrame without making a new copy
Tag : python , By : TheMoo
Date : November 26 2020, 01:01 AM

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

Tag : python , By : delphiace
Date : March 29 2020, 07:55 AM
wish helps you 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)
In [1]: import pandas as pd

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

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

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

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

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

Pandas: Append rows to DataFrame already running through pandas.DataFrame.apply

Tag : python , By : user181945
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I don't think there is a way to use apply the way you envision. And even if there were a way,
import pandas as pd

def crawl(url_stack):
    url_stack = list(url_stack)
    result = []
    while url_stack:
        url = url_stack.pop()
        scraped_urls = ...

        something_else = "foobar"
        result.append([url, something_else])
    return pd.DataFrame(result, columns=["URL", "Something else"])

df = pd.read_csv(spreadsheet.csv, delimiter=",")
df = crawl(df['URL'][::-1])
df.to_csv("result.csv", delimiter=",")

Python pandas: Append rows of DataFrame and delete the appended rows

Tag : python , By : user86493
Date : March 29 2020, 07:55 AM
I hope this helps . You can use isin with cumsum for Series, which is use for groupby with apply join function:
s = df.id.where(df.id.isin(L)).ffill().astype(int)
df1 = df.groupby(s)['text'].apply(''.join).reset_index()
print (df1)
   id          text
0   1        abczxc
1   3     qweasfefe
2   6  ertpoiwereer
3  10        poywqr
s = df.id.where(df.id.isin(L)).ffill().astype(int)
print (s)
0      1
1      1
2      3
3      3
4      3
5      6
6      6
7      6
8      6
9     10
10    10
Name: id, dtype: int32

How do I calculate mean on filtered rows of a pandas dataframe and append means to all columns of original dataframe?

Tag : python-2.7 , By : Thaweesak Suksuwan
Date : March 29 2020, 07:55 AM
I wish this help you How can I calculate all column's mean to ONLY rows that aren't equal to zero and append a new row at the bottom with the averages with only one line of code? It doesn't have to be one line, but I'm wondering why this doesn't work? , As John Galt commented need '0' because 0 is string:
df = df.append(df[(df.bar != '0')].mean(numeric_only=True), ignore_index=True)
print (df)
    foo   bar    total
0  foo1  bar1  293.090
1  foo2     0    0.000
2  foo3  bar3  342.300
3   NaN   NaN  317.695
s = df[(df.bar != '0')].mean(numeric_only=True).reindex(df.columns, fill_value='')
df = df.append(s, ignore_index=True)
print (df)
    foo   bar    total
0  foo1  bar1  293.090
1  foo2     0    0.000
2  foo3  bar3  342.300
3              317.695
df.loc[len(df.index)] = s
print (df)
    foo   bar    total
0  foo1  bar1  293.090
1  foo2     0    0.000
2  foo3  bar3  342.300
3              317.695

write rows in pandas dataframe and append it to existing dataframe

Tag : python , By : gorbiz
Date : March 29 2020, 07:55 AM
wish of those help I have the output of my script as year and the count of word from an article in that particular year : , Something like this should do it:
#!/usr/bin/env python 

def mkdf(filename):
    def combine(term, l):
        d = {"term": term}
        d.update(dict(zip(l[::2], l[1::2])))
        return d

    term = None
    other = []
    with open(filename) as I:
        n = 0
        for line in I:
            line = line.strip()
            except Exception as e:
                # not an int
                if term:    # if we have one, create the record
                     yield combine(term, other)

                term = line
                other = []
                n = 0
                if n > 0:
            n += 1

        # and the last one 
        yield combine(term, other)

if __name__ == "__main__":
    import pandas as pd
    import sys

    df = pd.DataFrame([r for r in mkdf(sys.argv[1])])
  2013 2014  term
0  118   23  abcd
1    1   45   xyz
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