I have a particular problem, I would like to clean and prepare my data and I have a lot of unknown values for the "highpoint_metres" column of my dataframe (members). As there is no missing information for the "peak_id", I calculated the median value of the height according to the peak_id to be more accurate.
I would like to do two steps: 1) add a new column to my "members" dataframe where there would be the value of the median but different depending on the "peak_id" (value calculated thanks to the code in the question). 2) That the code checks that the value in highpoint_metres is null, if it is, that the value of the new column is put instead. I don't know if this is clearer
code :
import pandas as pd
members = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv")
print(members)
mediane_peak_id = members[["peak_id","highpoint_metres"]].groupby("peak_id",as_index=False).median()
And I don't know how to continue from there (my level of python is very bad ;-))
I believe that's what you're looking for:
import numpy as np
import pandas as pd
members = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv")
median_highpoint_by_peak = members.groupby("peak_id")["highpoint_metres"].transform("median")
is_highpoint_missing = np.isnan(members.highpoint_metres)
members["highpoint_meters_imputed"] = np.where(is_highpoint_missing, median_highpoint_by_peak, members.highpoint_metres)
so one way to go about replacing 0 with median could be:
import numpy as np
df[col_name] = df[col_name].replace({0: np.median(df[col_name])})
You can also use apply function:
df[col_name] = df[col_name].apply(lambda x: np.median(df[col_name]) if x==0 else x)
Let me know if this helps.
So adding a little bit more info based on Marie's question.
One way to get median is through groupby and then left join it with the original dataframe.
df_gp = df.groupby(['peak_id']).agg(Median = (highpoint_metres, 'median')).reset_index()
df = pd.merge(df, df_gp, on='peak_id')
df = df.apply(lambda x['highpoint_metres']: x['Median'] if x['highpoint_metres']==np.nan else x['highpoint_metres'])
Let me know if this solves your issue
---Hello, everyone! New student of Python's Pandas here.
I have a dataframe I artificially constructed here: https://i.stack.imgur.com/cWgiB.png. Below is a text reconstruction.
df_dict = {
'header0' : [55,12,13,14,15],
'header1' : [21,22,23,24,25],
'header2' : [31,32,55,34,35],
'header3' : [41,42,43,44,45],
'header4' : [51,52,53,54,33]
}
index_list = {
0:'index0',
1:'index1',
2:'index2',
3:'index3',
4:'index4'
}
df = pd.DataFrame(df_dict).rename(index = index_list)
GOAL:
I want to pull the index row(s) and column header(s) of any ARBITRARY value(s) (int, float, str, etc.). So for eg, if I want the values of 55, this code will return: header0, index0, header2, index2 in some format. They could be list or tuple or print, etc.
CLARIFICATIONS:
Imagine the dataframe is of a large enough size that I cannot "just find it manually"
I do not know how large this value is in comparison to other values (so a "simple .idxmax()" probably won't cut it)
I do not know where this value is column or index wise (so "just .loc,.iloc where the value is" won't help either)
I do not know whether this value has duplicates or not, but if it does, return all its column/indexes.
WHAT I'VE TRIED SO FAR:
I've played around with .columns, .index, .loc, but just can't seem to get the answer. The farthest I've gotten is creating a boolean dataframe with df.values == 55 or df == 55, but cannot seem to do anything with it.
Another "farthest" way I've gotten is using df.unstack.idxmax(), which would return a tuple of the column and header, but has 2 major problems:
Only returns the max/min as per the .idxmax(), .idxmin() functions
Only returns the FIRST column/index matching my value, which doesn't help if there are duplicates
I know I could do a for loop to iterate through the entire dataframe, tracking which column and index I am on in temporary variables. Once I hit the value I am looking for, I'll break and return the current column and index. Was just hoping there was a less brute-force-y method out there, since I'd like a "high-speed calculation" method that would work on any dataframe of any size.
Thanks.
EDIT: Added text database, clarified questions.
Use np.where:
r, c = np.where(df == 55)
list(zip(df.index[r], df.columns[c]))
Output:
[('index0', 'header0'), ('index2', 'header2')]
There is a function in pandas that gives duplicate rows.
duplicate = df[df.duplicated()]
print(duplicate)
Use DataFrame.unstack for Series with MultiIndex and then filter duplicates by Series.duplicated with keep=False:
s = df.unstack()
out = s[s.duplicated(keep=False)].index.tolist()
If need also duplicates with values:
df1 = (s[s.duplicated(keep=False)]
.sort_values()
.rename_axis(index='idx', columns='cols')
.reset_index(name='val'))
If need tet specific value change mask for Series.eq (==):
s = df.unstack()
out = s[s.eq(55)].index.tolist()
So, in the code below, there is an iteration. However, it doesn't iterate over the whole DataFrame, but it just iterates over the columns, and then use .any() to check if there is any of the desierd value. Then using loc feature in the pandas it locates the value, and finally returns the index.
wanted_value = 55
for col in list(df.columns):
if df[col].eq(wanted_value).any() == True:
print("row:", *list(df.loc[df[col].eq(wanted_value)].index), ' col', col)
I have a spreadsheet with fields containing a body of text.
I want to calculate the Gunning-Fog score on each row and have the value output to that same excel file as a new column. To do that, I first need to calculate the score for each row. The code below works if I hard key the text into the df variable. However, it does not work when I define the field in the sheet (i.e., rfds) and pass that through to my r variable. I get the following error, but two fields I am testing contain 3,896 and 4,843 words respectively.
readability.exceptions.ReadabilityException: 100 words required.
Am I missing something obvious? Disclaimer, I am very new to python and coding in general! Any help is appreciated.
from readability import Readability
import pandas as pd
df = pd.read_excel(r"C:/Users/name/edgar/test/item1a_sandbox.xls")
rfd = df["Item 1A"]
rfds = rfd.to_string() # to fix "TypeError: expected string or buffer"
r = Readability(rfds)
fog = r.gunning_fog()
print(fog.score)
TL;DR: You need to pass the cell value and are currently passing a column of cells.
This line rfd = df["Item 1A"] returns a reference to a column. rfd.to_string() then generates a string containing either length (number of rows in the column) or the column reference. This is why a TypeError was thrown - neither the length nor the reference are strings.
Rather than taking a column and going down it, approach it from the other direction. Take the rows and then pull out the column:
for index, row in df.iterrows():
print(row.iloc[2])
The [2] is the column index.
Now a cell identifier exists, this can be passed to the Readability calculator:
r = Readability(row.iloc[2])
fog = r.gunning_fog()
print(fog.score)
Note that these can be combined together into one command:
print(Readability(row.iloc[2]).gunning_fog())
This shows you how commands can be chained together - which way you find it easier is up to you. The chaining is useful when you give it to something like apply or applymap.
Putting the whole thing together (the step by step way):
from readability import Readability
import pandas as pd
df = pd.read_excel(r"C:/Users/name/edgar/test/item1a_sandbox.xls")
for index, row in df.iterrows():
r = Readability(row.iloc[2])
fog = r.gunning_fog()
print(fog.score)
Or the clever way:
from readability import Readability
import pandas as pd
df = pd.read_excel(r"C:/Users/name/edgar/test/item1a_sandbox.xls")
print(df["Item 1A"].apply(lambda x: Readability(x).gunning_fog()))
My Pandas function is returning "None" as a result instead of the DataFrame that I am trying to filter using the function that I have written. Why is this so? And how can I resolve this? Thank you!
import pandas as pd
nz_data = pd.read_csv('research-and-development-survey-2016-2019-csv.csv', index_col = 2)
def count_of_mining_biz():
if "B_Mining" in nz_data[["Breakdown_category"]] and "Count of businesses" in nz_data[["Units"]]:
return nz_data.loc["2019", "RD_Value"]
print(count_of_mining_biz())
Here is how the data looks like.
I am trying to find out the RD Value in 2019 for the Mining industry. The reason why I have to set a conditional for the "Units" column is because there is another type of data that is not the count for the business mentioned.
.loc[..., ...] means .loc[row_index, col_index] but there's no row index called 2019.
Try using .loc with boolean masks in this case:
def count_of_mining_biz():
category = nz_data['Breakdown_category'] == 'B_Mining'
units = nz_data['Units'] == 'Count of businesses'
year = nz_data['Year'] == 2019
return nz_data.loc[category & units & year].RD_Value
I want to modify a large dataframe so that the remaining columns are features that contain only 2 unique values (eg, True and False) with the exception of the feature class (that contains more than 2 unique values).
I want to remove irrelevant features to simplify/clean the data set. But I need to keep the feature class which is called 'pattern' as this will be needed for predictions.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('Threat_Prediction_Clean.csv')
print (df.nunique())
if df.nunique() < 3:
dff = df[df.columns[df.nunique()<3]
elif df[df.columns == 'Pattern']:
dff.append(df[df.columns == 'Pattern'])
Expected result:
To have a new dataframe (called 'dff') which contains features of only 2 unique data values AND the 'pattern' feature
Actual result:
File "<ipython-input-33-ccbaf00f5866>", line 29
elif df[df.columns == 'Pattern']:
^
SyntaxError: invalid syntax
A few quick comments:
To reference a specific column of a dataframe you use df["col_name"] or df.col_name. So instead of your last elif statement you can just append df["Pattern"].The reason you get your error is because your elif statement never checks for a truth condition.
You are missing a closing bracket in your if statement. (See ForceBru's comment above.)
I don't understand what you are testing for in the if statement when you write df.nunique > 3. From what you wrote you want to preserve columns which have 2 unique values. What you have tests the entire dataframe. Try something like:
for col in df.columns:
if df[col].nunique() < 3:
#Append column