I am trying to use a pandas dataframe global variable. However, the dataframe is empty when I try to reassign or append it to the global variable. Any help appreciated.
import pandas as pd
df = pd.DataFrame()
def my_func():
global df
d = pd.DataFrame()
for i in range(10):
dct = {
"col1": i,
"col2": 'value {}'.format(i)
}
d.append(dct, ignore_index=True)
# df.append(dct, ignore_index=True) # Does not seem to append anything to the global variable
df = d # does not assign any values to the global variable
my_func()
df.head()
As opposed to list.append, pandas.DataFrame.append is not an in-place operation. Slightly changing your code works as expected:
import pandas as pd
df = pd.DataFrame()
def my_func():
global df
d = pd.DataFrame()
for i in range(10):
dct = {
"col1": i,
"col2": 'value {}'.format(i)}
d = d.append(dct, ignore_index=True) # <<< Assignment needed
# df.append(dct, ignore_index=True) # Does not seem to append anything to the global variable
df = d # does not assign any values to the global variable
my_func()
df.head()
Output:
col1 col2
0 0.0 value 0
1 1.0 value 1
2 2.0 value 2
3 3.0 value 3
4 4.0 value 4
Related
Say I have two DataFrames
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index = [0,1])
df2 = pd.DataFrame({'B':[8,9], 'C':[10,11]}, index = [1,2])
I want to merge so that any values in df1 are overwritten in there is a value in df2 at that location and any new values in df2 are added including the new rows and columns.
The result should be:
A B C
0 1 3 nan
1 2 8 10
2 nan 9 11
I've tried combine_first but that causes only nan values to be overwritten
updated has the issue where new rows are created rather than overwritten
merge has many issues.
I've tried writing my own function
def take_right(df1, df2, j, i):
print (df1)
print (df2)
try:
s1 = df1[j][i]
except:
s1 = np.NaN
try:
s2 = df2[j][i]
except:
s2 = np.NaN
if math.isnan(s2):
#print(s1)
return s1
else:
# print(s2)
return s2
def combine_df(df1, df2):
rows = (set(df1.index.values.tolist()) | set(df2.index.values.tolist()))
#print(rows)
columns = (set(df1.columns.values.tolist()) | set(df2.columns.values.tolist()))
#print(columns)
df = pd.DataFrame()
#df.columns = columns
for i in rows:
#df[:][i]=[]
for j in columns:
df = df.insert(int(i), j, take_right(df1,df2,j,i), allow_duplicates=False)
# print(df)
return df
This won't add new columns or rows to an empty DataFrame.
Thank you!!
One approach is to create an empty output dataframe with the union of columns and indices from df1 and df2 and then use the df.update method to assign their values into the out_df
import pandas as pd
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index = [0,1])
df2 = pd.DataFrame({'B':[8,9], 'C':[10,11]}, index = [1,2])
out_df = pd.DataFrame(
columns = df1.columns.union(df2.columns),
index = df1.index.union(df2.index),
)
out_df.update(df1)
out_df.update(df2)
out_df
Why does combine_first not work?
df = df2.combine_first(df1)
print(df)
Output:
A B C
0 1.0 3 NaN
1 2.0 8 10.0
2 NaN 9 11.0
I want to replace a row in a csv file with a variable. The row itself also has to be a variable. The following code is an example:
import pandas as pd
# sample dataframe
df = pd.DataFrame({'A': ['a','b','c'], 'B':['b','c','d']})
print("Original DataFrame:\n", df)
x = 1
y = 12698
df_rep = df.replace([int(x),1], y)
print("\nAfter replacing:\n", df_rep)
This can be done using pandas indexing eg df.iloc[row_num, col_num].
#update df
df.iloc[x,1]=y
#print df
print(df)
A B
0 a b
1 b 12698
2 c d
When I use the DataFrame.assign() method in my own function foobar, it has no effect to the global DataFrame.
#!/usr/bin/env python3
import pandas as pd
def foobar(df):
# has no affect to the "global" df
df.assign(Z = lambda x: x.A + x.B)
return df
data = {'A': range(3),
'B': range(3)}
df = pd.DataFrame(data)
df = foobar(df)
# There is no 'Z' column in this df
print(df)
The result output
A B
0 0 0
1 1 1
2 2 2
I assume this has something to do with the difference of views and copy's in Pandas. But I am not sure how to handle this the right and elegant Pandas-way.
Pandas assign returns a DataFrame so you need to assign the result to the same df. Try this:
def foobar(df):
df = df.assign(Z = lambda x: x.A + x.B)
return df
I created a dataframe df = pd.DataFrame({'col':[1,2,3,4,5,6]}) and I would like to take some values and put them in another dataframe df2 = pd.DataFrame({'A':[0,0]})by creating new columns.
I created a new column 'B' df2['B'] = df.iloc[0:2,0] and everything was fine, but then i created another column C df2['C'] = df.iloc[2:4,0] and there were only NaN values. I don't know why and if I print print(df.iloc[2:4]) everything is normal.
full code:
import pandas as pd
df = pd.DataFrame({'col':[1,2,3,4,5,6]})
df2 = pd.DataFrame({'A':[0,0]})
df2['B'] = df.iloc[0:2,0]
df2['C'] = df.iloc[2:4,0]
print(df2)
print('\n',df.iloc[2:4])
output:
A B C
0 0 1 NaN
1 0 2 NaN
col
2 3
3 4
Assignement df2['C'] = df.iloc[2:4,0] does not work as expected, because index is not the same. You can skip this using .values attributes.
import pandas as pd
df = pd.DataFrame({'col':[1,2,3,4,5,6]})
df2 = pd.DataFrame({'A':[0,0]})
df2['B'] = df.iloc[0:2,0]
df2['C'] = df.iloc[2:4,0].values
print(df2)
Is it possible to append to an empty data frame that doesn't contain any indices or columns?
I have tried to do this, but keep getting an empty dataframe at the end.
e.g.
import pandas as pd
df = pd.DataFrame()
data = ['some kind of data here' --> I have checked the type already, and it is a dataframe]
df.append(data)
The result looks like this:
Empty DataFrame
Columns: []
Index: []
This should work:
>>> df = pd.DataFrame()
>>> data = pd.DataFrame({"A": range(3)})
>>> df = df.append(data)
>>> df
A
0 0
1 1
2 2
Since the append doesn't happen in-place, so you'll have to store the output if you want it:
>>> df = pd.DataFrame()
>>> data = pd.DataFrame({"A": range(3)})
>>> df.append(data) # without storing
>>> df
Empty DataFrame
Columns: []
Index: []
>>> df = df.append(data)
>>> df
A
0 0
1 1
2 2
And if you want to add a row, you can use a dictionary:
df = pd.DataFrame()
df = df.append({'name': 'Zed', 'age': 9, 'height': 2}, ignore_index=True)
which gives you:
age height name
0 9 2 Zed
You can concat the data in this way:
InfoDF = pd.DataFrame()
tempDF = pd.DataFrame(rows,columns=['id','min_date'])
InfoDF = pd.concat([InfoDF,tempDF])
The answers are very useful, but since pandas.DataFrame.append was deprecated (as already mentioned by various users), and the answers using pandas.concat are not "Runnable Code Snippets" I would like to add the following snippet:
import pandas as pd
df = pd.DataFrame(columns =['name','age'])
row_to_append = pd.DataFrame([{'name':"Alice", 'age':"25"},{'name':"Bob", 'age':"32"}])
df = pd.concat([df,row_to_append])
So df is now:
name age
0 Alice 25
1 Bob 32
pandas.DataFrame.append Deprecated since version 1.4.0: Use concat() instead.
Therefore:
df = pd.DataFrame() # empty dataframe
df2 = pd..DataFrame(...) # some dataframe with data
df = pd.concat([df, df2])