Hello,
I am analyzing the next dataset with this information .
The column ['program_number'] is an object but I want to change it to a integer colum.
I have tried to replace some values but it doesn´t work.
as you can see, some values like 6 is duplicate. like '6 ' and 6.
How can I resolve it? Many thanks
UPDATE
Didn't see 1X and 3X at first.
If you need those numbers and just want to remove the X then:
df["Program"] = df["Program"].str.strip(" X").astype(int)
If there is data in the column which aren't numbers or which shouldn't be converted, you can use pd.to_numeric with errors='corece'. If there are cells which can't be converted, you'll get NaN. Be aware that this will result in floating numbers.
df["Program"] = pd.to_numeric(df["Program"], errors="coerce")
old
You want to use str.strip() here, rather than replace.
Try this:
df1['program_number'] = df1['program_number'].str.strip().astype(int)
Related
I want to compare two columns actual_data and pipeline_data based on source column bcz every source has different format.
I am trying to achieve the result column based on comparision between actual_data and pipeline_data .
I am new to pandas and looking for a way to implement this.
df['result'] = np.where(df['pipeline_data'].str.len() == df['actual_data'].str.len(), 'Match', np.where(df['pipeline_data'].str.len() > df['actual_data'].str.len(), 'Length greater than actual_data', 'Length shorter than actual_data'))
The code above should to what you want to do.
I now have some data, it's may contain null values
I want to delete it's null value (a whole row or a whole column)
How can I deal with the comparison?
Here is my data
https://reurl.cc/5lONv6
it will have some null values in the time series data
following is my code
c=pd.read_csv('./in/historical_01A190.txt',error_bad_lines=False)
c.dropna(axis=0,how='any',inplace=True)
c.dropna(axis=1,how='any',inplace=True)
c.to_csv('./out/historical_01A190.txt',index=False)
but it's didn't work
anyone can help me?
Okay, first of all, your data isn't saved as a csv. It's saved as a tab-separated file.
So you need to open it using pd.read_table
>>> c=pd.read_table('./data.txt',error_bad_lines=False,sep='\t')
Second, your data is full of nans -- if you use dropna on either rows or columns, you end up with just one row or column (dates) left. But using the correct opener on your file, the dropna and to_csv functions work.
If you don't assing the variable then it will only create a view which is not stored in memory.
c = c.dropna(axis=0,how='any',inplace=True)
c = c.dropna(axis=1,how='any',inplace=True)
c = c.to_csv('./out/historical_01A190.txt',index=False)
Try this.
I have a data frame, lets say xyz. I have written code to find out the % of null values each column possess in the dataframe. my code below:
round(100*(xyz.isnull().sum()/len(xyz.index)), 2)
let say i got following results:
abc 26.63
def 36.58
ghi 78.46
I want to drop column ghi because it has more than 70% of null values.
I achieved it using the following code:
xyz = xyz.drop(xyz.loc[:,round(100*(xyz.isnull().sum()/len(xyz.index)), 2)>70].columns, 1)
but , i did not understand how does this code works, can anyone please explain it?
the code is doing the following:
xyz.drop( [...], 1)
removes the specified elements for a given axis, either by row or by column. In this particular case, df.drop( ..., 1) means you're dropping by axis 1, i.e, column
xyz.loc[:, ... ].columns
will return a list with the column names resulting from your slicing condition
round(100*(xyz.isnull().sum()/len(xyz.index)), 2)>70
this instruction is counting the number of nulls, adding them up and normalizing by the number of rows, effectively computing the percentage of nan in each column. Then, the amount is rounded to have only 2 decimal positions and finally you return True is the number of nan is more than 70%. Hence, you get a mapping between columns and a True/False array.
Putting everything together: you're first producing a Boolean array that marks which columns have more than 70% nan, then, using .loc you use Boolean indexing to look only at the columns you want to drop ( nan % > 70%), then using .columns you recover the name of such columns, which then are used by the .drop instruction.
Hopefully this clear things up!
If you code is hard to understand , you can just check dropna with thresh, since pandas already cover this case.
df=df.dropna(axis=1,thresh=round(len(df)*0.3))
There is an inconsistency with dataframes that I cant explain. In the following, I'm not looking for a workaround (already found one) but an explanation of what is going on under the hood and how it explains the output.
One of my colleagues which I talked into using python and pandas, has a dataframe "data" with 12,000 rows.
"data" has a column "length" that contains numbers from 0 to 20. she wants to divided the dateframe into groups by length range: 0 to 9 in group 1, 9 to 14 in group 2, 15 and more in group 3. her solution was to add another column, "group", and fill it with the appropriate values. she wrote the following code:
data['group'] = np.nan
mask = data['length'] < 10;
data['group'][mask] = 1;
mask2 = (data['length'] > 9) & (data['phraseLength'] < 15);
data['group'][mask2] = 2;
mask3 = data['length'] > 14;
data['group'][mask3] = 3;
This code is not good, of course. the reason it is not good is because you dont know in run time whether data['group'][mask3], for example, will be a view and thus actually change the dataframe, or it will be a copy and thus the dataframe would remain unchanged. It took me quit sometime to explain it to her, since she argued correctly that she is doing an assignment, not a selection, so the operation should always return a view.
But that was not the strange part. the part the even I couldn't understand is this:
After performing this set of operation, we verified that the assignment took place in two different ways:
By typing data in the console and examining the dataframe summary. It told us we had a few thousand of null values. The number of null values was the same as the size of mask3 so we assumed the last assignment was made on a copy and not on a view.
By typing data.group.value_counts(). That returned 3 values: 1,2 and 3 (surprise) we then typed data.group.value_counts.sum() and it summed up to 12,000!
So by method 2, the group column contained no null values and all the values we wanted it to have. But by method 1 - it didnt!
Can anyone explain this?
see docs here.
You dont' want to set values this way for exactly the reason you pointed; since you don't know if its a view, you don't know that you are actually changing the data. 0.13 will raise/warn that you are attempting to do this, but easiest/best to just access like:
data.loc[mask3,'group'] = 3
which will guarantee you inplace setitem
I have just tried my first sqlite select-statement and got a result (an iterator over tuples). So, in other words, every row is represented by a tuple and I can access value in the cells of the row like this: r[7] or r[3] (get value from the column 7 or column 3). But I would like to access columns not by their positions but by their names. Let us say, I would like to know the value in the column user_name. What is the way to do it?
I found the answer on my question here:
cursor.execute("PRAGMA table_info(tablename)")
print cursor.fetchall()