I want to extract some columns maybe just 10 from a datafram with 30 columns but I'm not finding any code or functions to do it, I tried with iloc but not good results at all, help please here is my data frame:
So I just want to get the columns 1 to 10:
df1_10 = df.columns['1'....'10']
If you want to fetch 10 columns from your dataset then use this piece of code
df.iloc[:,1:11] # this will give you 10 columns
df.iloc[:,1:10] # this will give you only 9 columns.
# This is what you use in your code that's why you don't get the desired result.
Related
I am using a pandas dataset and need to delete rows that have a value of 3 or less. So if there are 12 columns and only 9 are populated with information that row needs to be deleted.
If this is confusing let me know and I will explain it another way.
Thanks good people
Edit
This is the code I have tried so far. It gives a syntax error.
indexnames = dataset.row[<= 3].index
dataset.drop(indexnames, inplace=True)
Try this:
New_df= Base_df.iloc[Base_df[Base_df.isnull().sum(axis=1)>3].index]
New_df
I have a CSV file whose columns are frequency counts of words, and whose rows are time periods. I want to sum for each column the total frequencies. Then I want to write to a CSV file for sums greater than or equal to 30, the column and row values, thus dropping columns whose sums are less than 30.
Just learning python and pandas. I know it is a simple question, but my knowledge is at that level. Your help is most appreciated.
I can read in the CSV file and compute the column sums.
df = pd.read_csv('data.csv')
Except of data file containing 3,874 columns and 100 rows
df.sum(axis = 0, skipna = True)
Excerpt of sums for columns
I am stuck on how to create the output file so that it looks like the original file but no longer has columns whose sums were less than 30.
I am stuck on how to write to a CSV file each row for each column whose sums are greater than or equal to 30. The layout of the output file would be the same as for the input file. The sums would not be included in the output.
Thanks very much for your help.
So, here is a link showing an excerpt of a file containing 100 rows and 3,857 columns:
It's easiest to do this in two steps:
1. Filter the DataFrame to just the columns you want to save
df_to_save = df.loc[:, (df.sum(axis=0, skipna=True) >= 30)]
.loc is for picking rows/columns based either on labels or conditions; the syntax is .loc[rows, columns], so : means "take all the rows", and then the second part is the condition on our columns - I've taken the sum you'd given in your question and set it greater than or equal to 30.
2. Save the filtered DataFrame to CSV
df_to_save.to_csv('path/to/write_file.csv', header=True, index=False)
Just put your filepath in as the first argument. header=True means the header labels from the table will be written back out to the file, and index=False means the numbered row labels Pandas automatically created when you read in the CSV won't be included in the export.
See this answer here: How to delete a column in pandas dataframe based on a condition? . Note, the solution for your question doesn't need isnull() before the sum(), as that is specific to their question for counting NaN values.
I have a pyspark dataframe as follows in the picture:
I.e. i have four columns: year, word, count, frequency. The year is from 2000 to 2015.
I could like to have some operation on the (pyspark) dataframe so that i get the result in a format as the following picture:
The new dataframe column should be : word, frequency_2000, frequency_2001, frequency_2002, ..., frequency_2015.
With the frequency of each word in each year coming from previous dataframe.
Any advice how I could write efficient code?
Also, please rename the title if you could come up some more informative.
After some research, I found a solution:
Now, the crosstab function can get the output directly :
topw_ys.crosstab("word", "year").toPandas()
Results:
word_year 2000 2015
0 mining 10 6
1 system 11 12
...
I am trying to preset the dimensions of my data frame in pandas so that I can have 500 rows by 300 columns. I want to set it before I enter data into the dataframe.
I am working on a project where I need to take a column of data, copy it, shift it one to the right and shift it down by one row.
I am having trouble with the last row being cut off when I shift it down by one row (eg: I started with 23 rows and it remains at 23 rows despite the fact that I shifted down by one and should have 24 rows).
Here is what I have done so far:
bolusCI = pd.DataFrame()
##set index to very high number to accommodate shifting row down by 1
bolusCI = bolus_raw[["Activity (mCi)"]].copy()
activity_copy = bolusCI.shift(1)
activity_copy
pd.concat([bolusCI, activity_copy], axis =1)
Thanks!
There might be a more efficient way to achieve what you are looking to do, but to directly answer your question you could do something like this to init the DataFrame with certain dimensions
pd.DataFrame(columns=range(300),index=range(500))
You just need to define the index and columns in the constructor. The simplest way is to use pandas.RangeIndex. It mimics np.arange and range in syntax. You can also pass a name parameter to name it.
pd.DataFrame
pd.Index
df = pd.DataFrame(
index=pd.RangeIndex(500),
columns=pd.RangeIndex(300)
)
print(df.shape)
(500, 300)
What is the most efficient way to forward fill information in a large dataframe?
I combined about 6 million rows x 50 columns of dimensional data from daily files. I dropped the duplicates and now I have about 200,000 rows of unique data which would track any change that happens to one of the dimensions.
Unfortunately, some of the raw data is messed up and has null values. How do I efficiently fill in the null data with the previous values?
id start_date end_date is_current location dimensions...
xyz987 2016-03-11 2016-04-02 Expired CA lots_of_stuff
xyz987 2016-04-03 2016-04-21 Expired NaN lots_of_stuff
xyz987 2016-04-22 NaN Current CA lots_of_stuff
That's the basic shape of the data. The issue is that some dimensions are blank when they shouldn't be (this is an error in the raw data). An example is that for previous rows, the location is filled out for the row but it is blank in the next row. I know that the location has not changed but it is capturing it as a unique row because it is blank.
I assume that I need to do a groupby using the ID field. Is this the correct syntax? Do I need to list all of the columns in the dataframe?
cols = [list of all of the columns in the dataframe]
wfm.groupby(['id'])[cols].fillna(method='ffill', inplace=True)
There are about 75,000 unique IDs within the 200,000 row dataframe. I tried doing a
df.fillna(method='ffill', inplace=True)
but I need to do it based on the IDs and I want to make sure that I am being as efficient as possible (it took my computer a long time to read and consolidate all of these files into memory).
It is likely efficient to execute the fillna directly on the groupby object:
df = df.groupby(['id']).fillna(method='ffill')
Method referenced
here
in documentation.
How about forward filling each group?
df = df.groupby(['id'], as_index=False).apply(lambda group: group.ffill())
github/jreback: this is a dupe of #7895. .ffill is not implemented in cython on a groupby operation (though it certainly could be), and instead calls python space on each group.
here's an easy way to do this.
url:https://github.com/pandas-dev/pandas/issues/11296
according to jreback's answer, when you do a groupby ffill() is not optimized, but cumsum() is. try this:
df = df.sort_values('id')
df.ffill() * (1 - df.isnull().astype(int)).groupby('id').cumsum().applymap(lambda x: None if x == 0 else 1)