You could run one apply() which will use for-loop to convert list to Series with headers – it can use enumerate to put correct number in headers. Because some rows have less participants so it puts NaN which you can later fill with empty strings. And next you can use join() to add all as … Read more
The following could be used to plot your data. The main point is that you need to specify the (rather unusual) format of the datetimes (“%Y/%m/%d/%H/%M”), such that it can be converted to a datetime object. import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv(“data/minuteData.csv”) df[“minute”] = pd.to_datetime(df[“minute”], format=”%Y/%m/%d/%H/%M”) plt.plot(df[“minute”],df[“spreadprice”], label=”spreadprice” ) plt.plot(df[“minute”],df[“bollup”], … Read more
Given (stealing from an earlier problem today): “”” IndexID IndexDateTime IndexAttribute ColumnA ColumnB 1 2015-02-05 8 A B 1 2015-02-05 7 C D 1 2015-02-10 7 X Y “”” import pandas as pd import numpy as np df = pd.read_clipboard(parse_dates=[“IndexDateTime”]).set_index([“IndexID”, “IndexDateTime”, “IndexAttribute”]) df: ColumnA ColumnB IndexID IndexDateTime IndexAttribute 1 2015-02-05 8 A B 7 C … Read more
You can define the bins and cuts as follows: bins = [9 * i for i in range(0, df[‘age’].max() // 9 + 2)] cuts = pd.cut(df[‘age’], bins, right=False) print(cuts) 0 [18, 27) 1 [18, 27) 2 [54, 63) 3 [27, 36) 4 [45, 54) Name: age, dtype: category Categories (7, interval[int64, left]): [[0, 9) < … Read more
So the question is how to reorder your columns in a custom way. For example you have the following DF and you want to reorder your columns in the following way (indices): 5, 3, rest… DF In [82]: df Out[82]: A B C D E F G 0 1 0 0.5 5 1 7 6 … Read more
If your top dataframe image is what you have already then this is a simple problem. df[‘State’].fillna(method=’ffill’) 0 solved Repeat the value in column until a change occurs
@cᴏʟᴅsᴘᴇᴇᴅ explain it better: When two datetime objects are subtracted, the result is a timedelta. Depending on which date was larger, the result could be positive or negative. Also if all values in column have no times, in pandas are not shown. Patient[“Waiting”] = Patient[“Appointment”] – Patient[“Scheduled”] 2016-04-29 00:00:00 – 2016-04-29 18:38:08 For remove negative … Read more
You can try to pivot the table. Which may give the format you require. Considering the information you gave as as ActionsOnly.csv userId,movieId,rating 18,9,3 32,204,4 49,2817,1 62,160438,4 70,667,5 73,1599,1 73,4441,3 73,4614,3.5 73,86142,4 95,4636,2 103,71,1 118,3769,4 150,4866,2 You wan to find out what user rated what movie out of 5. The userId is the index column, … Read more
This is a stack trace. It shows not just where the actual error occurred directly, but also what the program was doing while it occurred. The code df.loc[‘origin’ == ‘US’] (on line 6) is at the top of the trace, meaning it’s the root cause, but this by itself is not an error. This will … Read more
You could use split etc. but it’s not very robust: >>> s.str[2:-2].str.split(“‘,\s*'”, expand=True).add_prefix(‘rep’) rep0 … rep2 1 f, they have everything i am looking for. … q, i always find what I am looking for 2 easy to navigate … easy to use, very convenient [2 rows x 3 columns] The robust way to do … Read more
Damn, a quick google search finds: def convert_si_to_number(x): total_stars = 0 x = x.replace(‘€’, ”) if ‘k’ in x: if len(x) > 1: total_stars = float(x.replace(‘k’, ”)) * 1000 # convert k to a thousand elif ‘M’ in x: if len(x) > 1: total_stars = float(x.replace(‘M’, ”)) * 1000000 # convert M to a million … Read more
try this, filtered_column=df.filter(regex=r’^p’,axis=1).columns.values df=df.drop(filtered_column, axis=1) As you didn’t provide a sample data I created this my self. Input: p101 p102 p103 q21 qw32 kwp 0 68 17 54 67 93 36 1 32 22 56 69 38 6 2 58 48 89 68 60 79 3 64 14 63 53 7 86 4 67 94 … Read more
you have to use df.unique(). That’s why pandas is used. You are not supposed to loop through pandas. Even for applying manipulations to the data inside dataframe you should go for df.apply(). This should get the list of unique date values as a list: df.Date.unique() In case, you want to use loop: Use df.iterrows() : … Read more
If you need to upload into csv files separately during that iteration then below code follows, If this is what your looking or else please review your question for j in range(0, 500): for k in range(1,9): try: file_name=”Result_”+str(i)+’_’+str(j)+’.csv’ df1 = url_base_1 + str(j) + url_base_2 df2 = make_dataframe(df1.format(year), int(k)) print(k) df2.to_csv(file_name,encoding=’utf-8′, index=False) except: pass … Read more
Let’s try this df[“val1”] = df[“val1”].transform(lambda x: x.fillna(x.mean(), limit=2)) df[“val2”] = df[“val2”].transform(lambda x: x.fillna(x.mean(), limit=2)) print df Don’t forget to let us know if it solved your problem 🙂 1 solved how to fill NA with mean only for 2 or less consequective values of NA