AttributeError: ‘NumPy Int64’ Object Has No Attribute ‘To_PyDateTime’ | Fix It Now

Why this matters: to_pydatetime() belongs to pandas date-time objects (like Timestamp, DatetimeIndex, or a Series’ .dt accessor). A raw numpy.int64 has no such method. When integers represent epoch time, you must convert them into real datetimes before handing them to anything that expects Python datetime objects.

What The Error Really Means

This crash happens when code tries to call .to_pydatetime() on values that are not pandas date-time objects. The method exists on pandas.Timestamp and on a datetime-typed Series via .dt.to_pydatetime(), where it returns native Python datetime objects. On a plain numpy.int64—even if the integer encodes a timestamp—there’s no such attribute, so Python raises the AttributeError.

• Know the owners: Timestamp.to_pydatetime() converts a single pandas timestamp to a Python datetime.
• Use the accessor: Series.dt.to_pydatetime() turns a datetime Series into Python datetime objects.
• Avoid guessing: If your column is plain int64, convert with pd.to_datetime first, then call .dt.to_pydatetime() if you actually need Python objects.

Quick Checks To Confirm The Root Cause

Quick check: Print dtypes and the first values before calling any conversion. It takes seconds and prevents blind fixes.

# Inspect dtypes
print(df.dtypes)

# Spot the offender
print(type(df["ts"].iloc[0]))  # numpy.int64? Timestamp? numpy.datetime64?

# Check if you accidentally grabbed a NumPy array (drops pandas dtype info)
print(type(df["ts"].values))   # numpy.ndarray means you lost the pandas dtype

• If you see int64: Your “timestamps” are raw integers (seconds/ms/ns from epoch). Convert with pd.to_datetime using the right unit.
• If you see numpy.datetime64: You already have NumPy datetimes. Either keep them, or cast to pandas datetime dtype and proceed.
• If you see Timestamp: You can safely call .dt.to_pydatetime() (Series) or .to_pydatetime() (single Timestamp).

Correct Conversions For Common Cases

Use the right unit and keep the path short. This table gives the exact call for each source format.

Source Values	Symptom	Safe Conversion
Seconds since epoch (e.g., 1697040000)	int64; AttributeError on to_pydatetime	df["ts"] = pd.to_datetime(df["ts"], unit="s", utc=True)
Milliseconds since epoch	int64; wrong date if unit omitted	df["ts"] = pd.to_datetime(df["ts"], unit="ms", utc=True)
Nanoseconds since epoch	int64; values look huge	df["ts"] = pd.to_datetime(df["ts"], unit="ns", utc=True)
numpy.datetime64 dtype	No to_pydatetime on ints, but here you have datetimes	df["ts"] = pd.to_datetime(df["ts"]) (already datetime-like)
YYYYMMDD integers (e.g., 20231011)	Parsed as plain numbers	df["date"] = pd.to_datetime(df["date"], format="%Y%m%d")

Deeper fix: After conversion, decide if you need Python datetime objects. Many pandas and plotting APIs accept pandas datetimes directly, so calling to_pydatetime() is often unnecessary work.

Attributeerror ‘Numpy Int64’ No Attribute To_PyDateTime — Causes And Fixes

You’ll hit AttributeError: ‘NumPy Int64’ Object Has No Attribute ‘To_PyDateTime’ most often in these patterns:

• Lost the pandas dtype: Calling df["ts"].values returns a NumPy array. If the underlying dtype was plain int64, you now have raw ints. Keep the Series, or call pd.to_datetime on it before conversion.
• Wrong unit: Treating milliseconds as seconds (or nanoseconds as milliseconds) silently shifts dates. Always set unit explicitly.
• Blind casting: Using .astype("datetime64[ns]") on integer epoch data can fail or misinterpret values. Prefer pd.to_datetime(..., unit=...), which is designed for epoch conversion.
• Library expects a DatetimeIndex: Analytics/finance helpers often require an index of type DatetimeIndex. If you pass a numeric index, internal calls to to_pydatetime() blow up.

Reliable Fix Recipes You Can Trust

Fix 1 — Convert Epoch Integers To Pandas Datetime

# seconds
df["ts"] = pd.to_datetime(df["ts"], unit="s", utc=True)

# milliseconds
df["ts"] = pd.to_datetime(df["ts"], unit="ms", utc=True)

# nanoseconds
df["ts"] = pd.to_datetime(df["ts"], unit="ns", utc=True)

• Keep UTC consistent: Set utc=True when timestamps are UTC. Localize/convert later with .dt.tz_convert("America/New_York") if needed.

Fix 2 — Build A Proper DatetimeIndex

df = df.set_index(pd.to_datetime(df["ts"], unit="s", utc=True)).sort_index()
# many libraries now accept df.index directly

• Avoid .values here: Stay with the pandas index to keep datetime typing intact.

Fix 3 — From NumPy Datetime64 To Python Datetime (Only If Required)

# If you already have a pandas datetime Series:
py_datetimes = df["ts"].dt.to_pydatetime()  # array of Python datetime

# Single Timestamp
py_dt = pd.Timestamp("2024-10-11 00:00:00Z").to_pydatetime()

• Prefer pandas objects: Most plotting/stats stacks consume pandas datetimes directly; skip converting to Python datetime unless an API demands it.

Fix 4 — YYYYMMDD Or Other Integer Date Codes

df["date"] = pd.to_datetime(df["date"], format="%Y%m%d")

• Use format for non-epoch integers: This avoids expensive inference and date mistakes.

Fix 5 — When A Library Calls to_pydatetime() Internally

Some helpers expect a DatetimeIndex and will call to_pydatetime() inside their code paths. If you hand them numeric indices, they break. Set a datetime index before the call:

rets = rets.copy()
rets.index = pd.to_datetime(rets.index, unit="s", utc=True)  # or matching unit
lib.create_report(rets)  # stops failing on to_pydatetime()

End-To-End Example That Avoids The Trap

Scenario: You loaded trades with a ts column as epoch milliseconds and want a time-indexed DataFrame for analysis and charts—without ever touching numpy.int64.to_pydatetime.

import pandas as pd

# Raw input
df = pd.DataFrame({
    "ts": [1715731200000, 1715817600000, 1715904000000],  # ms
    "price": [101.2, 103.0, 102.4]
})

# 1) Convert to pandas datetime (explicit unit!)
df["ts"] = pd.to_datetime(df["ts"], unit="ms", utc=True)

# 2) Set as index for downstream libraries
df = df.set_index("ts").sort_index()

# 3) Plot or pass to libraries that expect datetimes; no .to_pydatetime() needed
ax = df["price"].plot(title="Price Over Time")

Frequently Missed Details That Keep The Error Away

• Stay in pandas land: Use the Series/DataFrame directly. Pulling out .values too early throws away pandas’ datetime dtype.
• Be explicit with units: Seconds vs. milliseconds vs. nanoseconds is the easiest way to get dates wildly off. Set unit every time you convert epoch numbers.
• Convert once: Don’t bounce between pandas, NumPy, and Python datetime without a reason. Each hop can drop timezone/nanoseconds.
• Only call to_pydatetime() when required: Many APIs are faster with pandas datetimes. Converting to Python objects adds overhead.

Why This Fix Matches The Libraries’ Expectations

Most time-series tools assume either a DatetimeIndex or a datetime-typed column. Passing a numeric index leaves them guessing, so they probe for conversion helpers like to_pydatetime(). If those helpers land on int64 data, your code raises AttributeError: ‘NumPy Int64’ Object Has No Attribute ‘To_PyDateTime’. Converting early with pd.to_datetime, then indexing on the result, aligns the data shape with what those tools want and removes the crash.

Copy-Paste Fix Checklist

1. Inspect dtypes — print(df.dtypes). Is the “time” column int64?
2. Convert with the right unit — pd.to_datetime(df["ts"], unit="s"|"ms"|"ns", utc=True).
3. Make it the index — df = df.set_index("ts").sort_index() if the next function expects an index.
4. Only if needed — df.index.to_pydatetime() or df["ts"].dt.to_pydatetime() for APIs that demand Python objects.
5. Never call on ints — Don’t call to_pydatetime() on numpy.int64 values.

Where To Go From Here

Quick pointer: Keep a tiny helper that enforces the conversion rule in one place:

def ensure_datetime(series, unit=None, tz="UTC"):
    if series.dtype.kind in {"i", "u"} and unit:
        out = pd.to_datetime(series, unit=unit, utc=True)
    else:
        out = pd.to_datetime(series, utc=True)
    return out.dt.tz_convert(tz) if tz else out

# Usage
df["ts"] = ensure_datetime(df["ts"], unit="ms")

This keeps your pipelines neat, your types predictable, and avoids calling to_pydatetime() on anything that isn’t a pandas datetime.

When you see AttributeError: ‘NumPy Int64’ Object Has No Attribute ‘To_PyDateTime’ inside a plotting or backtest report, fix the dtype at the boundary and re-run. If an external tool still throws the same message, set a proper DatetimeIndex before passing the data; that’s the contract those tools expect.