Develop Reference

Python Return all Columns [duplicate]

Using Python Pandas I am trying to find the Country & Place with the maximum value.
This returns the maximum value:
data.groupby(['Country','Place'])['Value'].max()
But how do I get the corresponding Country and Place name?

Assuming df has a unique index, this gives the row with the maximum value:
In [34]: df.loc[df['Value'].idxmax()]
Out[34]:
Country US
Place Kansas
Value 894
Name: 7
Note that idxmax returns index labels. So if the DataFrame has duplicates in the index, the label may not uniquely identify the row, so df.loc may return more than one row.
Therefore, if df does not have a unique index, you must make the index unique before proceeding as above. Depending on the DataFrame, sometimes you can use stack or set_index to make the index unique. Or, you can simply reset the index (so the rows become renumbered, starting at 0):
df = df.reset_index()

df[df['Value']==df['Value'].max()]
This will return the entire row with max value

I think the easiest way to return a row with the maximum value is by getting its index. argmax() can be used to return the index of the row with the largest value.
index = df.Value.argmax()
Now the index could be used to get the features for that particular row:
df.iloc[df.Value.argmax(), 0:2]

The country and place is the index of the series, if you don't need the index, you can set as_index=False:
df.groupby(['country','place'], as_index=False)['value'].max()
Edit:
It seems that you want the place with max value for every country, following code will do what you want:
df.groupby("country").apply(lambda df:df.irow(df.value.argmax()))

Use the index attribute of DataFrame. Note that I don't type all the rows in the example.
In [14]: df = data.groupby(['Country','Place'])['Value'].max()
In [15]: df.index
Out[15]:
MultiIndex
[Spain Manchester, UK London , US Mchigan , NewYork ]
In [16]: df.index[0]
Out[16]: ('Spain', 'Manchester')
In [17]: df.index[1]
Out[17]: ('UK', 'London')
You can also get the value by that index:
In [21]: for index in df.index:
print index, df[index]
....:
('Spain', 'Manchester') 512
('UK', 'London') 778
('US', 'Mchigan') 854
('US', 'NewYork') 562
Edit
Sorry for misunderstanding what you want, try followings:
In [52]: s=data.max()
In [53]: print '%s, %s, %s' % (s['Country'], s['Place'], s['Value'])
US, NewYork, 854

In order to print the Country and Place with maximum value, use the following line of code.
print(df[['Country', 'Place']][df.Value == df.Value.max()])

You can use:
print(df[df['Value']==df['Value'].max()])

Using DataFrame.nlargest.
The dedicated method for this is nlargest which uses algorithm.SelectNFrame on the background, which is a performant way of doing: sort_values().head(n)
x y a b
0 1 2 a x
1 2 4 b x
2 3 6 c y
3 4 1 a z
4 5 2 b z
5 6 3 c z
df.nlargest(1, 'y')
x y a b
2 3 6 c y

import pandas
df is the data frame you create.
Use the command:
df1=df[['Country','Place']][df.Value == df['Value'].max()]
This will display the country and place whose value is maximum.

My solution for finding maximum values in columns:
df.ix[df.idxmax()]
, also minimum:
df.ix[df.idxmin()]

I'd recommend using nlargest for better performance and shorter code. import pandas
df[col_name].value_counts().nlargest(n=1)

I encountered a similar error while trying to import data using pandas, The first column on my dataset had spaces before the start of the words. I removed the spaces and it worked like a charm!!

python for loop using index to create values in dataframe

I have a very simple for loop problem and I haven't found a solution in any of the similar questions on Stack. I want to use a for loop to create values in a pandas dataframe. I want the values to be strings that contain a numerical index. I can make the correct value print, but I can't make this value get saved in the dataframe. I'm new to python.
# reproducible example
import pandas as pd
df1 = pd.DataFrame({'x':range(5)})
# for loop to add a row with an index
for i in range(5):
print("data_{i}.txt".format(i=i)) # this prints the value that I want
df1['file'] = "data_{i}.txt".format(i=i)
This loop prints the exact value that I want to put into the 'file' column of df1, but when I look at df1, it only uses the last value for the index.
x file
0 0 data_4.txt
1 1 data_4.txt
2 2 data_4.txt
3 3 data_4.txt
4 4 data_4.txt
I have tried using enumerate, but can't find a solution with this. I assume everyone will yell at me for posting a duplicate question, but I have not found anything that works and if someone points me to a solution that solves this problem, I'll happily remove this question.

There are better ways to create a DataFrame, but to answer your question:
Replace the last line in your code:
df1['file'] = "data_{i}.txt".format(i=i)
with:
df1.loc[i, 'file'] = "data_{0}.txt".format(i)
For more information, read about the .loc here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html
On the same page, you can read about accessors like .at and .iloc as well.

You can do list-comprehension:
df1['file'] = ["data_{i}.txt".format(i=i) for i in range(5)]
print(df1)
Prints:
x file
0 0 data_0.txt
1 1 data_1.txt
2 2 data_2.txt
3 3 data_3.txt
4 4 data_4.txt
OR at the creating of DataFrame:
df1 = pd.DataFrame({'x':range(5), 'file': ["data_{i}.txt".format(i=i) for i in range(5)]})
print(df1)
OR:
df1 = pd.DataFrame([{'x':i, 'file': "data_{i}.txt".format(i=i)} for i in range(5)])
print(df1)

I've found success with the .at method
for i in range(5):
print("data_{i}.txt".format(i=i)) # this prints the value that I want
df1.at[i, 'file'] = "data_{i}.txt".format(i=i)
Returns:
x file
0 0 data_0.txt
1 1 data_1.txt
2 2 data_2.txt
3 3 data_3.txt
4 4 data_4.txt

when you assign a variable to a dataframe column the way you do -
using the df['colname'] = 'val', it assigns the val across all rows.
That is why you are seeing only the last value.
Change your code to:
import pandas as pd
df1 = pd.DataFrame({'x':range(5)})
# for loop to add a row with an index
to_assign = []
for i in range(5):
print("data_{i}.txt".format(i=i)) # this prints the value that I want
to_assign.append(data_{i}.txt".format(i=i))
##outside of the loop - only once - to all dataframe rows
df1['file'] = to_assign.
As a thought, pandas has a great API for performing these type of actions without for loops.
You should start practicing those.

Creating a new column but creates copy of dataframe

I would like to check the value of the row above and see it it is the same as the current row. I found a great answer here: df['match'] = df.col1.eq(df.col1.shift()) such that col1 is what you are comparing.
However, when I tried it, I received a SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. warning. My col1 is a string. I know you can suppress warnings but how would I check the same row above and make sure that I am not creating a copy of the dataframe? Even with the warning I do get my desired output, but was curious if there exists a better way.
import pandas as pd
data = {'col1':['a','a','a','b','b','c','c','c','d','d'],
'week':[1,1,1,1,1,2,2,2,2,2]}
df = pd.DataFrame(data, columns=['col1','week'])
df['check_condition'] = 1
while sum(df.check_condition) != 0:
for week in df.week:
wk = df.loc[df.week == week]
wk['match'] = wk.col1.eq(wk.col1.shift()) # <-- where the warning occurs
# fix the repetitive value...which I have not done yet
# for now just exit out of the while loop
df.loc[df.week == week,'check_condition'] = 0

You can't ignore a pandas SettingWithCopyWarning!
It's 100% telling you that your code is not going to work as intended, if at all. Stop, investigate and fix it. (It's not an ignoreable thing you can filter out, like a pandas FutureWarning nagging about deprecation.)
Multiple issues with your code:
You're trying to iterate over a dataframe (but not with groupby()), take slices of it (in the subdataframe wk, which yes is a copy of a slice)...
then assign to the (nonexistent) new column wk['match']. This is bad, you shouldn't do this. (You could initialize df['match'] = np.nan, but it'd still be wrong to try to assign to the copy in wk)...
SettingWithCopyWarning is being triggered when you try to assign to wk['match']. It's telling you wk is a copy of a slice from dataframe df, not df itself. Hence like it tells you: A value is trying to be set on a copy of a slice from a DataFrame. That assignment would only get thrown away every time wk gets overwritten by your loop, so even if you could force it to work on wk it would be wrong. That's why SettingWithCopyWarning is a code smell you shouldn't be making a copy of a slice of df in the first place.
Later on, you also try to assign to column df['check_condition'] while iterating over the df, that's also bad.
Solution:
df['check_condition'] = df['col1'].eq(df['col1'].shift()).astype(int)
df
col1 week check_condition
0 a 1 0
1 a 1 1
2 a 1 1
3 b 1 0
4 b 1 1
5 c 2 0
6 c 2 1
7 c 2 1
8 d 2 0
9 d 2 1
More generally, for more complicated code where you want to iterate over each group of dataframe according to some grouping criteria, you'd use use groupby() and split-apply-combine instead.
you're grouping by wk.col1.eq(wk.col1.shift()), i.e. rows where col1 value doesn't change from the preceding row
and you want to set check_condition to 0 on those rows
and 1 on rows where col1 value did change from the preceding row
But in this simpler case you can skip groupby() and do a direct assignment.

Value counts for specific items in a DataFrame

I have a dataframe (df) of messages that appears similar the following:
From To
person1#gmail.com stranger1#gmail.com
person2#gmail.com stranger1#gmail.com, stranger2#gmail.com
person3#gmail.com person1#gmail.com, stranger2#gmail.com
I want to count the amount of times each email appears from a specific list. My list being:
lst = ['person1#gmail.com', 'stranger2#gmail.com', 'person3#gmail.com']
I'm hoping to receive a dataframe/series/dictionary with a result like this:
list_item Total_Count
person1#gmail.com 2
stranger2#gmail.com 2
person3#gmail.com 1
I'm tried several different things, but haven't succeeded. I thought I could try something like the for loop below (it returns a Syntax Error), but I cannot figure out the right way to write it.
for To,From in zip(df.To, df.From):
for item in lst:
if To,From contains item in emails:
Count(item)
Should this type of task be accomplished with a for loop or are there out of the box pandas methods that could solve this easier?

stack-based
Split your To column, stack everything and then do a value_counts:
v = pd.concat([df.From, df.To.str.split(', ', expand=True)], axis=1).stack()
v[v.isin(lst)].value_counts()
stranger2#gmail.com 2
person1#gmail.com 2
person3#gmail.com 1
dtype: int64
melt
Another option is to use melt:
v = (df.set_index('From')
.To.str.split(', ', expand=True)
.reset_index()
.melt()['value']
)
v[v.isin(lst)].value_counts()
stranger2#gmail.com 2
person1#gmail.com 2
person3#gmail.com 1
Name: value, dtype: int64
Note that set_index + str.split + reset_index is synonymous to pd.concat([...])...

Pandas - Add column containing metadata about the row

I want to add a column to a Dataframe that will contain a number derived from the number of NaN values in the row, specifically: one less than the number of non-NaN values in the row.
I tried:
for index, row in df.iterrows():
count = row.value_counts()
val = sum(count) - 1
df['Num Hits'] = val
Which returns an error:
-c:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
and puts the first val value into every cell of the new column. I've tried reading about .loc and indexing in the Pandas documentation and failed to make sense of it. I gather that .loc wants a row_index and a column_index but I don't know if these are pre-defined in every dataframe and I just have to specify them somehow or if I need to "set" an index on the dataframe somehow before telling the loop where to place the new value, val.

You can totally do it in a vectorized way without using a loop, which is likely to be faster than the loop version:
In [89]:
print df
0 1 2 3
0 0.835396 0.330275 0.786579 0.493567
1 0.751678 0.299354 0.050638 0.483490
2 0.559348 0.106477 0.807911 0.883195
3 0.250296 0.281871 0.439523 0.117846
4 0.480055 0.269579 0.282295 0.170642
In [90]:
#number of valid numbers - 1
df.apply(lambda x: np.isfinite(x).sum()-1, axis=1)
Out[90]:
0 3
1 3
2 3
3 3
4 3
dtype: int64
#DSM brought up an good point that the above solution is still not fully vectorized. A vectorized form can be simply (~df.isnull()).sum(axis=1)-1.

You can use the index variable that you define as part of the for loop as the row_index that .loc is looking for:
for index, row in df.iterrows():
count = row.value_counts()
val = sum(count) - 1
df.loc[index, 'Num Hits'] = val